If you’ve ever wondered how autonomous vehicles detect obstacles in real-time, how engineers survey complex terrains from aircraft, or how cities monitor vegetation around power lines, the answer frequently involves LiDAR.

This article offers a deep dive into what LiDAR is, how it works, the core components of a LiDAR system, and its wide-ranging applications across industries—from geospatial mapping and transportation to agriculture and environmental science.

What Is LiDAR?

LiDAR stands for Light Detection and Ranging. It is an active remote sensing technology that uses eye-safe laser pulses to measure distances between the sensor and objects in its path. Unlike passive sensors, which rely on ambient light, LiDAR actively emits its own energy—in the form of laser pulses—and measures the time it takes for each pulse to bounce back after hitting a surface.

The result is a precise, 3D “point cloud” that represents the surface features of an area, object, or environment. This spatial data can then be analyzed to measure distances, model surfaces, assess terrain, detect objects, and much more.

How Does LiDAR Work?

The core principle behind LiDAR is simple but powerful: Time of Flight (ToF).

• A laser pulse is emitted from the LiDAR system toward the ground or a target object.
• The pulse travels through space, hits the object, and is reflected back.
• The system records the return time of the pulse.
• Distance is calculated using the speed of light and the time it took for the round trip: Distance = Speed of Light × Travel Time / 2
• GPS and IMU systems within the LiDAR platform determine the exact position and orientation of the sensor at the time of each pulse.
• A computer aggregates this data, creating a 3D point cloud representing the physical environment.

By repeating this process hundreds of thousands of times per second, LiDAR generates an ultra-dense spatial dataset that can be used for detailed modeling and analysis.

Key Components of a LiDAR System

To function effectively, a LiDAR system integrates several core components:

• Laser Unit: Emits the light pulses (typically in the near-infrared or green spectrum) used for distance measurement.
• GPS Receiver: Tracks the exact geographic coordinates and altitude of the sensor platform.
• IMU (Inertial Measurement Unit): Monitors the pitch, roll, and yaw of the sensor platform (especially useful in aerial LiDAR to compensate for aircraft movement).
• Receiver: Detects the returning light signals.
• LiDAR Processing Unit (LPU): Converts timing and signal data into 3D coordinates.
• Computer System: Stores and processes the collected data to generate usable outputs.

These components work together in perfect harmony, whether mounted on a drone, aircraft, terrestrial vehicle, or even a satellite.

LiDAR vs. Radar vs. Sonar

While LiDAR may sound similar to radar or sonar, the difference lies in the type of waves each system uses:

The shorter wavelength of laser light allows LiDAR to achieve far higher resolution and precision than radar or sonar, making it ideal for mapping and object detection in fine detail.

How LiDAR Measures Through Trees

LiDAR’s ability to capture multiple returns from a single pulse makes it especially valuable in environments with canopy cover or complex surfaces.

• First return: May reflect off the treetop.
• Intermediate returns: Could hit branches or lower leaves.
• Last return: Might reach the forest floor or ground surface.

This capability enables scientists and engineers to understand forest structure, estimate vegetation density, or even map terrain under dense foliage—something optical cameras often struggle with.

Types of LiDAR Systems

LiDAR systems vary based on their platform and operational context:

• Airborne LiDAR: Mounted on aircraft or drones, ideal for topographic and vegetation mapping over large areas.
• Terrestrial LiDAR: Ground-based, often used for architectural surveys, construction monitoring, and infrastructure inspection.
• Mobile LiDAR: Installed on moving vehicles like cars or trains to collect data from roadways, tunnels, and urban environments.
• Bathymetric LiDAR: Uses green lasers that penetrate water to map underwater surfaces, such as riverbeds, lakes, and coastlines.

Real-World Applications of LiDAR

LiDAR’s capabilities have found widespread application across diverse sectors. Here are some of the most impactful use cases:

1. Autonomous Vehicles

LiDAR helps self-driving cars perceive their environment by detecting road edges, vehicles, pedestrians, and obstacles in real time. This data is crucial for path planning, object avoidance, and vehicle navigation.

2. Topographic Mapping

Governments, environmental agencies, and surveyors use LiDAR to create highly accurate elevation models of landforms. These are useful for infrastructure planning, watershed analysis, and disaster response planning.

3. Forestry and Environmental Monitoring

LiDAR provides insights into forest canopy height, biomass estimation, and tree density. It also helps in monitoring deforestation, habitat changes, and ecological health.

4. Utility Infrastructure

Energy companies deploy LiDAR to monitor vegetation encroachment near power lines and to inspect pipelines, railways, and telecommunication assets.

5. Construction and Building Information Modeling (BIM)

In the AEC (Architecture, Engineering, and Construction) sector, LiDAR supports 3D scanning of buildings, terrain analysis, and structural integrity assessments.

6. Mining and Exploration

LiDAR is used to monitor pit slopes, compute material volumes, detect geologic features, and improve site safety.

7. Agriculture

In precision farming, LiDAR enables farmers to analyze terrain variations, optimize irrigation patterns, and deploy autonomous farming equipment more effectively.

8. Public Safety and Security

Security systems integrate LiDAR for intrusion detection without capturing identifiable imagery—making it GDPR-compliant in regions with strict privacy laws like the EU.

The Rise of 4D LiDAR

Emerging 4D LiDAR systems not only provide spatial (3D) data but also include velocity measurements—adding a fourth dimension to the dataset. This is especially transformative in scenarios like:

• Collision detection in autonomous vehicles
• Real-time hazard identification in manufacturing
• Dynamic tracking of moving objects in surveillance systems

Privacy, Ethics, and Compliance

While LiDAR does not capture images like traditional cameras, it can still raise privacy concerns in some regions. However, its privacy-preserving nature—recording only spatial data without facial recognition—makes it increasingly attractive for applications governed by privacy frameworks like the General Data Protection Regulation (GDPR) in Europe.

Challenges and Considerations

Despite its many benefits, implementing a LiDAR system involves a few challenges:

• Cost: High-end LiDAR systems can be expensive, although prices are dropping due to increased demand and production.
• Data Volume: The point cloud data generated is large and requires significant storage and processing power.
• Environmental Factors: Rain, fog, and certain surface materials can affect LiDAR accuracy.

However, as edge computing and rugged industrial systems evolve, these challenges are being addressed, allowing broader adoption in harsh or remote environments.

Final Thoughts

LiDAR is revolutionizing how we understand and interact with the physical world—from measuring tree heights in rainforests to enabling autonomous navigation through city streets. Its fusion of precision, speed, and versatility makes it a cornerstone of modern sensing technology.

As innovation continues, the next wave—4D LiDAR and AI-driven analytics—will unlock even greater potential across smart cities, environmental monitoring, industrial automation, and beyond.

Whether you’re a tech enthusiast, researcher, or engineer, LiDAR isn’t just another acronym—it’s a window into a data-rich, spatially-aware future.

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Notably, LiDAR, which stands for Light Detection and Ranging, is one of the most advanced and most accurate technologies in a Geographic Information System (GIS) used in agriculture.

It is a remote sensing method that measures variable distances of the Earth by using light in the form of a pulsed laser. LiDAR can smartly improve the agriculture system with 3D modeling, yield forecasting, soil type determination, and phenomics study. Even in difficult weather and lighting conditions, it provides a precise 3D measurement of a water flow, water catchment area, location of tree and crop, and their accurate plant population, water flow direction at the base of each tree.

Below are some of the key advantages of using LiDAR in agriculture.

1. 3D modelling of crop field

LiDAR technology can create 3D models of agricultural land and precise maps of the local natural resources. Electronic measurements of canopy traits, greenness, chlorophyll content, soil map, and water 3D map appear to be the most accurate way to provide trustworthy and objective information about the management of crops. The operating parameters of agronomic applications will be immediately modified using this information in real-time. In every instance, LiDAR seems to be the method for measuring canopies that is the most precise. The application of pesticides and irrigation systems are just two examples where the 3D modeling of the crops is crucial. To improve pesticide application, canopy characterization is also crucial in horticulture.

GLS was used to separate maize plants from weeds and soil in field crops so that herbicides could be applied selectively. GLS was used to better apply nitrogen fertilizers by sensing the wheat plants’ nitrogen status. Another method uses GLS to estimate wheat crop density, which could be used to automatically adjust a combined harvester’s speed for a constant intake of biomass.

2. Phenomics study

Plant phenomics is a new way to connect environmental research with plant genomics, which will help with plant management and breeding. “High-throughput plant phenotyping” has improved thanks to remote sensing techniques. Three-dimensional (3D) phenotyping’s accuracy, effectiveness, and applicability remain problematic, particularly in field settings. With the quick development of facilities and algorithms, light detection and ranging (LiDAR) offers a potent new tool for 3D phenotyping.

Numerous initiatives have been made to use LiDAR in agriculture to study static and dynamic changes in structural and functional phenotypes. With easier and less expensive gene association, analysis of environmental practices, and new insights into breeding and management, this advancement also enhances 3D plant modeling across various spatial-temporal scales and disciplines.

3. Determination of soil type and soil analysis

Data can be gathered using LiDAR technology to pinpoint the precise type of soil present on a given farmland. The farmer needs to know this information because it tells them what kinds of crops can be grown on that farm and how much fertilizer is needed. It may benefit 5R stewardship (right time, dose, amount, place, and method). LiDAR sensors can assist experts and farmers in analyzing the soil type and content to determine whether it is suitable for growing crops.

LiDAR data can be used to precisely design and map farmland, as well as to map other types of land. The layout and topography of the farmland will also be included in this data. As per the study, a 2D mean profile view of the soil is created to compare the digital LiDAR. The experiments led to the following conclusion: By increasing the resolution from 2D measurements to 3D scans, the geometric variations of soil texture, water content, flow, slope, and fertility in the direction of the soil are observed more clearly. The findings thus demonstrated the significance of using a non-contact method for precise measurements of soil surface.

4. Smart crop management

Maps of cultivated fields are typically created by manually digitizing the fields using satellite or aerial imagery. Manual ground surveys are then used to assign these images to various crop types. This approach, however, is time-consuming, costly, and prone to human error. Automated remote sensing techniques are more affordable options, like those used by airborne LiDARs. The data gathered with LiDAR can be combined with machine learning algorithms to automate crop classification.

LiDAR can analyze crops, estimate crop quality, compare results to standards, and determine whether a crop is suitable for a given location. By utilizing LiDAR in agriculture, farmers will better understand the current choice of agricultural soil, which crops are suitable for farming at the current stage, and other environmental information about farmland through intelligent analysis and better management. LiDAR also aids in determining the extent to which crops have been damaged and aids farmers in developing a recovery strategy.

5. Yield forecasting

It has been possible to predict expected farm yields using LiDAR data. Farmers can make quicker and more accurate harvesting decisions thanks to information about field yield variability provided by yield monitoring and crop geometric characterization. Fruit maturation can be identified by farmers using LiDAR technology. LiDAR sensors can help farmers increase yield by estimating the yield through field scanning.

Researchers discovered that using LiDAR sensors mounted on drones; they could predict rice yield. Several empirical yield prediction models for rice production were created using five pertinent vegetation indices, including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), rice growth vegetation index (RGVI), moisture stress index (MSI), and leaf area index (LAI).

6. Prevention of soil erosion

The GLS point cloud is used to measure surface properties (roughness) in the runoff zone, which are impossible to measure in the field through 2D mapping and modeling of specific farmland. LiDAR data can be used to quantify soil loss. Farmers can devise preventive measures to lessen or stop soil erosion by learning the exact terrain of the farm and its contours.

7. LiDAR in forestry

Forests can be mapped using LiDAR by measuring the canopy’s vertical organization and density. These models enable us to produce precise forest inventories and comprehend complex structures. LiDAR can track the patterns of forest fires and alert the fire department to the potential occurrence of the next forest fire.

We might be able to boost the site’s productivity regarding the quality of the trees and the overall yield using precision forestry, which is focused on particular forest areas. A ground-based LiDAR system has great potential for determining structural characteristics like volume and forest inventory variables like DBH and tree height. The outcomes demonstrate that GLS can determine forest inventory parameters in a precise, high-resolution, non-destructive manner.

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LiDAR offers many advantages over traditional methods for representing a terrain surface. The advantages include accuracy, cost, and resolution. One most attractive characteristics of LiDAR is its very high vertical accuracy which enables it to represent the Earth’s surface with high accuracy.

The LiDAR raw data include everything on the ground, such as buildings, telephone poles, power lines, and even birds. Laser returns are recorded no matter what target the laser happens to strike.

This post will briefly introduce a few more commonly used software, specifically designed for processing and analyzing LiDAR and remote sensing. Let’s take a look.

1. LAStools

LAStools are the fastest and most memory-efficient solution for batch-scripted multi-core LiDAR processing. They can turn billions of LiDAR points into valuable products at blazing speeds and with low memory requirements. LAStools offer every tool needed, from raw data processing to analysis and variable extraction. They run on the command line and provide a graphical user interface (GUI) for those not comfortable with command lines and batch scripting. Every tool (e.g., las2DTM or las2txt) comes with its own README file, which tells in principle what the device is used for and how it is used (inputs, outputs, parameters, etc.).

2. Fusion

The Silviculture and Forest Models Team, Research Branch of the US Forest Service, developed Fusion, a LiDAR viewing, and analysis software tool. Fusion also works with terrain data sets and IFSAR. A laser sensor with a transmitter and receiver, a geodetic-quality Global Positioning System (GPS) receiver, and an Inertial Navigation System (INS) unit are used in LiDAR. The laser sensor is attached to the aircraft’s underside. Once in the air, the sensor emits rapid pulses of infrared laser light used to calculate ranges to terrain points.

3. OPALS

OPALS stands for Orientation and Processing of Airborne Laser Scanning data. Researchers at Vienna University of Technology built by researchers a modular program system consisting of small components (modules) grouped together thematically in packages. It provides a complete processing chain for processing airborne laser scanning data (waveform decomposition, quality control, georeferencing, structure line extraction, point cloud classification, DTM generation) and several fields of application like forestry, hydrography, city modeling, and power lines).

4. Quantum GIS (QGIS)

Quantum GIS, or QGIS, is a free and open-source geographic information system. Licensed under the GNU General Public License, it is one of the most popular open-source GIS programs. QGIS processes LiDAR data via the LAS Tools software that can be installed as its own toolbox. The Open Source Geospatial Foundation has designated QGIS as an official project (OSGeo). It supports a wide range of vector, raster, and database formats and functions and runs on Linux, Unix, Mac OSX, Windows, and Android.

5. ENVI

ENVI has a range of functionality for GIS and remote sensing analyses in addition to its utility for processing and analyzing LiDAR datasets. ENVI is the industry standard for image processing and analysis, developed by Idaho State University. Image analysts, GIS professionals, and scientists use it to extract timely, accurate, and reliable data from geospatial imagery. It’s backed by science, simple to use, and tightly integrated with Esri’s ArcGIS platform. ENVI makes deep learning accessible to the general public through user-friendly tools and workflows that do not necessitate programming. ENVI can also be customized to meet specific project requirements using an API and visual programming environment.

6. ArcGIS

ESRI’s ArcGIS is one of the most widely used GIS software with unique capabilities and flexible licensing for applying location-based analytics to your business practices. It has its own tools for handling, processing, and analyzing LiDAR datasets. ArcGIS reads LAS files natively, allowing you to access lidar data right away without having to convert or import data. Lidar data in the form of LAS (or ASCII) files are supported by ArcGIS. The LAS dataset, terrain dataset, and mosaic dataset are three different formats (datasets) used to manage and work with your lidar data in ArcGIS, depending on your needs. In 2D and 3D, LAS attributes can be used to filter out content and symbolize points.

7. ERDAS IMAGINE

ERDAS IMAGINE is a widely used remote sensing software that also offers tools for LiDAR. It provides genuine value, consolidating remote sensing, photogrammetry, LiDAR analysis, basic vector analysis, and radar processing into a single product. ERDAS IMAGINE simplifies image classification and segmentation, orthorectification, mosaicking, reprojection, elevation extraction, and image interpretation. It offers K-Means, ISODATA, object-based image segmentation, Machine Learning, and Deep Learning Artificial Intelligence algorithms.

8. FugroViewer

FugroViewer is a powerful, easy-to-use freeware designed to help users make the most of their geospatial data. It is a fast and easy-to-use tool for visualizing LiDAR data, developed for use with various raster- and vector-based geospatial datasets, including photogrammetric, lidar, and IFSAR sources. FugroViewer can read files up to six times larger, with improved graphics handling to decrease data rendering and increase efficiency. FugroViewer now supports the American Society for Photogrammetry and Remote Sensing’s (ASPRS) latest LAS 1.4 open file format for lidar data storage and delivery.

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LiDAR has several advantages over traditional analog or digital passive optical remote sensing, including nearly perfect spatial data registration and the ability to penetrate the vertical profile of a forest canopy and quantify its structure.

LiDAR has been successfully used in many parts of the world to assess the height and size of individual trees or estimate canopy closure, volume, and biomass of forest stands at the stand level; to assess wildlife habitat and quantify stand susceptibility to fire. A clear advantage of LiDAR is that it is stable and consistent – it can produce products such as DTMs that are comparable and can be used across time and space.

In this post, we highlight some of the strengths and weaknesses of LiDAR.

Strengths of LiDAR

Most accurate 3D information: LiDAR is an airborne sensing technology that makes data collection fast and precise. LiDAR provides the most accurate and quick data on the 3D structure of any remote sensing technique. LiDAR gives a much higher surface density than other data collection methods such as photogrammetry, with low-pulse density LiDAR typically exhibiting sub-meter accuracy. LiDAR can also collect elevation data in a dense forest and can be used day and night. It is not affected by any geometry distortions such as angular landscapes or light variations such as darkness and light. LiDAR can be used to map inaccessible featureless areas such as high mountains and thick snow areas. Besides, it is not affected by extreme weather. This means that data can be collected under any conditions and sent for analysis.

Versatile data: LiDAR technology is a versatile technology that can be used integrated with other data sources to produce digital models of terrain and canopy and customized for diverse needs to suit the research questions at hand. It makes it easier to analyze complex data automatically with minimum human dependence, especially during the data collection and data analysis phase. LiDAR provides highly complementary data that can be used in conjunction with satellite and aerial imagery to gain insights that neither imagery nor LiDAR can reach alone.

Ability to cover large areas cheaply and quickly: It has quicker turnaround, lower costs than photogrammetric methods, and is less labor-intensive. It can also collect data in steep terrain and shadows and produce digital elevation models (DEM) and digital surface models (DSM). LiDAR is a cheaper remote sensing method in several applications, especially when dealing with vast land areas considering that it is fast and highly accurate.

Weaknesses of LiDAR

High cost and difficulties of LiDAR data collection: Although LiDAR is inexpensive when used in large applications, it can be costly when used to collect data in smaller areas. LiDAR costs decrease per unit area as the total area surveyed increases, but this can be substantial and depend on the cost of fuel, pilots, airplane rental, all of which depend on geography and weather. It also involves technically complex processing, analyzing, and interpretations, which can be time-consuming and technically challenging, that may require expertise in GIS and remote sensing to use and interpret correctly.

Limited spatial and temporal availability: LiDAR currently largely lacks multispectral data limits its utility to non-spectral analyses. Freely available LiDAR datasets are available for a small fraction of the world and are frequently limited to data collected at a single point in time.

LiDAR cannot penetrate thick canopies: Dense tropical forests are problematic due to a lack of ground hits. In some cases, where the forest canopy is dense, the LiDAR pulses may not penetrate the canopy, resulting in incomplete data. When collecting data, LiDAR pulses may not penetrate thick vegetation, resulting in inaccurate data. LiDAR cannot work on altitudes higher than 2000 meters because the pulses will not be effective at these heights.

Influences from the weather: LiDAR can be ineffective during heavy rain or low-hanging clouds. Because the laser pulses are based on reflection, LiDAR does not work well in areas or situations with high sun angles or large reflections. It may not return accurate data when used on water surfaces or where the surface is not uniform because high water depth will affect the reflection of the pulses.

Enormous datasets are difficult to interpret: LiDAR collects extremely large datasets that require extensive analysis and interpretation. As a result, analyzing the data could take a long time. Because of the enormous data sets and the complexity of the data being collected, it may require skilled techniques to analyze the information, which adds to the overall cost.

No International protocols: The Laser beams used by LiDAR pulses are usually powerful in some instances, affecting the human eye. Notably, when using LiDAR technology, there are no strict international protocols that guide data collection and analysis; as a result, it is done haphazardly.

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LiDAR (light detection and ranging) is a distance-measuring technique that employs a laser. A laser scanner sends out pulses of light. A portion of the photons emitted by the pulse is reflected back to the scanner when it hits a target. Since the direction of the pulse and the time between pulse emission and return are known factors, the 3D location from which the pulse is reflected is calculable. The laser produces a highly precise 3D point cloud (model) that can be used to estimate the 3D structure of the target area by emitting millions of such pulses and recording where they reflect.

How are LiDAR data collected?

The scanning laser is commonly mounted in an aircraft – usually a fixed-wing plane, but increasingly in drones – and scans the ground along the flight path or direction. For terrestrial-based laser (TLS) scans, scanning lasers are occasionally mounted on a tripod or vehicle.

What can LiDAR data do?

LiDAR data creates a detailed three-dimensional model of the target area, including terrain, topography, and vegetation. The digital terrain model of the surface can then be used to generate various additional products, such as slope or visibility models. The 3D data on the vegetative structure can be used for a variety of forestry and ecological applications.

Are LiDAR and Airborne Laser Scanning the same thing?

Airborne laser scanning, or ALS, is simply LiDAR data collection from an aircraft while it is in the air. If the LiDAR scanner is mounted on a tripod, the method is known as Terrestrial Laser Scanning or TLS.

What is the difference between a pulse and an echo?

The scanning laser produces a pulse. A clump of timestamped photons can be compared to the pulse. When the pulse hits a target, some photons reflect back as eco, which the LiDAR device recognizes. As a result, an echo source is a location where photons were reflected after being hit by a pulse. Multiple echoes can be produced by a single pulse, and this happens frequently.

What is the resolution of LiDAR data?

The data that is freely distributed most often has a density between 0.8 – 3 pulses / m2. This is termed pulse density. Pulse density is the number of emitted pulses per square meter. Another term is an echo- or return density, which refers to the number of echoes per square meter; a single pulse can produce multiple echoes. The data may have a pulse density of 10 pulses per m2 if an airplane flies at a low altitude of 200-600 meters. The data from Terrestrial Laser Scanning easily yield point densities of hundreds of pulses per square meter.

Do I need LiDAR, or will other data suffice?

Photogrammetry, satellite, or aerial imagery may be sufficient, depending on your goal. LiDAR, on the other hand, provides excellent precision 3D structural information about a target area (be it forest or terrain), albeit at a relatively high cost. LiDAR data is sometimes combined with other remote sensing data to produce insights that neither data could achieve on its own.

How can I acquire LiDAR data?

Many countries have already gathered large amounts of LiDAR data for topographic mapping and forestry, and these data are often freely available. If LiDAR data isn’t available for your area of interest, you’ll have to decide whether it’s worth the money to collect it.

How much does it cost to collect LiDAR data?

The most expensive part of ALS or airborne LiDAR is the aircraft operation, determined by factors such as the study site’s distance from the nearest airport, fuel costs, altitude, pilot time, and weather. These prices vary by geography and time, and the size of the target area makes it difficult to make broad estimates. As the size of the surveyed area grows larger, the cost per unit area typically decreases, and large scales can be quite cost-effective. The availability of drones lowers the cost of collecting small-scatter data, and terrestrial laser scanning systems are becoming more compact and affordable.

What do LiDAR data consist of?

Each received echo information is stored in the data. In a text (.txt) format, for example, the data is frequently structured so that one row contains information from one echo, such as its XYZ coordinates, intensity, and order if there are multiple echoes.

How are LiDAR data different from aerial or satellite imagery?

Aerial and satellite imagery, like a photograph without a flash, is considered passive because it records reflected sunlight. In other words, because the sensor does not emit any light, the light source is external. LiDAR, meanwhile, is an active remote sensing method thanks to its emitted light pulses. Furthermore, unlike imagery, LiDAR data does not contain spectral data; instead, LiDAR data contains structural information in 3D coordinates and the intensity of each echo.

Do I need to ground-truth LiDAR data?

Because LiDAR data are not interpreted as imagery and highly precise (most commonly within 5-15 cm), ground-truthing of the point cloud structure for 3D mapping is generally unnecessary. The ground-truthing may be necessary if you study something other than physical terrains, such as whether.

What format do the data come in, and how large are the datasets?

The most common format for LiDAR data is the compressed LAS/LAZ format. The LAS format is a standard for storing LiDAR data, and the LAZ format is a compressed version of the LAS format. The National Land Survey of Finland, for example, distributes LiDAR data in LAZ files, which contain LiDAR data from a 3 x 3 km area in a single file. The compressed LAZ file can be as small as 50 megabytes, but it can be several gigabytes in size when uncompressed and converted to a text file with tens of millions of rows (one for each echo).

Can the LiDAR data format be changed?

Many other formats, including plain ASCII files and ESRI shapefiles, can convert the data. The only practical limitation is size: loading and processing a point shapefile with 20 million points in GIS software would be extremely slow. Many common LiDAR processing programs include conversion tools.

What programs are available for processing LiDAR data?

Many programs have been tailored specifically for processing and analyzing LiDAR data. The most common GIS and remote sensing programs can process LiDAR data in LAS/LAZ format.

Can LiDAR data be used without any processing?

No, in most cases. The chain must scale the heights (Z-coordinates) of the LiDAR echoes to ground level during core processing. Each echo has a Z-coordinate that indicates its height above ground level, though the data is often processed for a fee by the service provider who collects it. Separating the ground echoes from the other echoes is the first step. Finally, subtract the terrain model from the height of the echo in the LiDAR data to create a terrain model from the ground echoes. This effectively scales all of the echoes to a scale that is above ground level.

How do I use LiDAR data in my analyses?

The first step is to define your target area and extract relevant, manageable data from a larger LiDAR data set. You’ll have to rescale the data frequently after that, as described in the previous question. Then you calculate relevant metrics from the available 3D point clouds, such as canopy height or density, for example. The adaptability of LiDAR data to suit individual needs and derive myriad metrics of 3D structure is its beauty, even though it is time-consuming and relatively complex to work with.

Can the point cloud data be converted into rasters like satellite images, for instance?

There’s no reason LiDAR data can’t be turned into rasters. LiDAR data is still frequently used to derive habitat structural variables, which are then converted to rasters. For example, in some forestry applications, it is common to divide the study area into 16 x 16-meter grid cells and extract LiDAR data from each cell. The point clouds within each cell are used to calculate forest structure variables, which are then incorporated into a raster with the same dimensions. Digital terrain and canopy models, as well as height models, are common raster products from LiDAR.

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It uses light in the form of a pulsed laser to measure ranges and generate precise, wide-range three-dimensional geospatial information about the object on the Earth and its surface characteristics.

LiDAR is generally recognized as a major technological revolution after the global positioning system and is the leading research foreland of high-precision surveying and mapping. LiDAR has passed through many impressive development stages since the first attempt to use laser for distance measurement. Let’s briefly look at the history and the evolution of LiDAR in the past decades.

History of LiDAR

LiDAR became very popular after the invention of Ruby Laser, created by Theodore Maiman in 1960. Theodore Maiman and team at Hughes Research Laboratory shined a high-power flash lamp on a ruby rod, triggering a beam of coherent light – the first laser.

It was used in range measurement in earth observation, environment monitor, and reconnaissance fields, because of high precision, high resolution, low volume, and easy usability, compared to general measurement methods.

Soon, similar to other technology, laser caught the attention of the military. Soon the U.S. Army began research into military laser devices, and the first military laser range finder passed military tests in 1961.

In 1971, the U.S. military initiated the world’s first Ruby laser ranging system: AN/GVS-3. This first-generation rangefinder consisted of a photomultiplier detector and red outer precious stone light exciter.

Due to the disadvantage of large volume, heavyweight, high power consumption, and other shortcomings, it was soon replaced by the second generation ranging system, which used near-infrared neodymium laser (mainly Nd: YAG laser) PIN photodiode or avalanche photodiode.

This technology became more mature, used in long, medium, and short-range laser ranging radar and became an inevitable trend as YAG laser technology was ripe in the 1970s.

In 1977, the United States developed the first hand-held Nd: YAG laser rangefinder: AN/gvs-5 with the size of the standard 7-50 military binoculars and a total weight of only 2kg. From the late 1970s to the mid-1980s, laser rangefinder became the largest procurement in the military laser market.

Firstly, laser ranging was mainly used in military and scientific research. It was rare in the industrial instrument as laser ranging sensors were too expensive, typically in the thousands of dollars; high prices have been the main reason hindering their widespread use. However, due to significant technological advances, the price has dropped to a few hundred dollars, making it possible to become a cost-effective means of detection for many long-range inspections in the future.

In the process of using, some shortcomings such as low accuracy under all-weather ranging poor compatibility and damage to the eyes were gradually exposed. Later, with laser and electronic techniques, a third-generation laser ranging system was introduced with smaller size, low power consumption, and high accuracy. Alongside, different kinds of ranging laser systems were developed, including single-beam, two-dimensional, three-dimensional laser ranging systems.

With new optical systems and signal processing in the 1980s, various products kept appearing in the 1990s. In 1996 Bushnell launched the 400 LD laser rangefinder Yaddaga400 with the range-finding capability of 400m, which was rated as one of the 100 important scientific and technological achievements in the world.

In 1998, Tasco developed an LD Laser range finder, a camera type with a range measuring capacity of 800m. Since 1995, the development of eye-safe semiconductor laser ranging technology has been very rapid; research on laser rangefinder has been carried out within the range of 800nm-900nm, with peak power of 10W, a pulse width of 20-50ns, repetition frequency of 1-10khz, and measurement distance of 10m-1km.

A high-precision laser rangefinder was developed and produced by BOSCH. It was small and easy to carry and use in real estate, interior decoration, construction, surveying and mapping, and other fields. The DLE150 laser rangefinder, developed by this company, can measure precisely and detect very quickly，its measurement range is between 0.3m to 150m. The measurement time is generally less than 0.5 seconds, with the accuracy down to ±3mm. Using advanced software technology, it can measure height, Angle, area, and volume.

In the 1980s, laser rangefinders developed by China Aerospace Science & Industry Corp achieved an accuracy of 0.5 meters within 200 meters. At the same time, satellite-borne ruby laser rangefinder and airborne laser rangefinder for complex terrain mapping were developed. Before 2007, most research on LiDAR remained at the stage of theoretical experiments in universities and research institutions; the main equipment depended on imported products.

Benefiting from the rapid development of the information industry and LiDAR technology, the market of hardware and data products in LiDAR has been growing at an annual rate of over 30%.

With the booming research of unmanned aerial vehicles (UAV), a series of emerging laser radar manufacturers have emerged, such as SureStart, RoboSense, LeiShen, SLAMTEC, and other start-ups. Their products were gradually recognized by the market and put into use in the field of autonomous driving.

After decades of evolution, LiDAR has become mainstream technology for surface data acquisition for various large-scale surveying applications. The overall LiDAR market is expected to reach $5.2 billion by 2022.

Applications of LiDAR

1. Large-scale basic terrain mapping

Compared with the ground survey or photogrammetry method, LiDAR technology captures the 3D coordinates easier and quicker; it gives accurate data of the elevation of the ground and shows the 3D imaging of the elevation. Using detailed terrain modeling, soil scientists can research the changes in landform breaks and changes in slope, which show a pattern in spatial relationships. Besides, a surface model created from LiDAR is used to add graphical value to maps. DEM (from LiDAR) is added underneath all layers that show the 3D view of the land. Especially LiDAR data (DEM) is added to aerial photography to show the 3D view, making it easier to plan roads, buildings, bridges, and rivers.

2. High-precision charting for autonomous navigation

A digital street map is a key to car navigation and visualization. More and more maps are rich in 3D information nowadays, which can be used for autonomous driving and smart city management. For instance, HERE Technologies collect point cloud data along streets from over 200 vehicles; each vehicle is equipped with a raised platform with a laser scanner, camera, and GNSS/IMU system. The scanner uses the Velodyne HDL-32E, which can collect up to 700,000 points per second with accuracy is about 2cm, providing important information for digital street maps, so a detailed route map is generated to analyze slopes and navigate.

3. Precise topographic surveying in vegetation coverage areas’

The measurement of the forest canopy is not easy. The data collected by measurement techniques are not usually accurate. LiDAR measures the height and density of vegetation on the ground, making it an ideal tool for studying vegetation over large areas.

LiDAR data is also used to derive information about vegetation structure, including Canopy Height, Canopy Cover, Leaf Area Index, Vertical Forest Structure, and Species identification (if a less dense forests with high point density LiDAR).

This data helps in extracting the exact information below:

• General forest management and planning – Providing data used to manage the forests. LiDAR can measure the tree qualities of the forests, mapping the degradation of the forests through human activities.
• Study of forest ecology and habitat – LiDAR comes in handy in collecting all the details of the forest ecology, including flora and fauna found in the forests and the species that can survive and those that cannot survive.
• Quantification of forest fire fuel – LiDAR comes in handy when trying to quantify the forest fire fuel. This data is important in predicting forest fires and developing mechanisms to mitigate or control forest fires in the future.

4. Emergency mapping

Because of the traffic stop, it is difficult to determine and measure the damages caused by earthquakes. With the help of LiDAR technology, it is now easy to collect the data before and after the earthquake occurs to assess the damage and benefit in future prevention. For instance, MA Hong-Chao and his team from Wuhan University use airborne LiDAR to gain precise DEM data acquired from the Tangjiashan and Yingxiu areas in Wenchuan Earthquake Relief works.

5. Military

LiDAR has several uses in the military. The military depends on LiDAR to map out the exact terrain of the battlefield and know the exact position of the enemy and their capacity. It can locate all enemy weaponry, including tankers, and help in neutralizing the threat on a much larger scale. For instance, Lincoln Laboratory develops a LiDAR device that produces high-resolution 3D images using short laser pulses and a focal-plane array of 32×32 Geiger-mode avalanche photodiodes with independent digital time-of-flight counting circuits at each pixel.

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Autonomous vehicle (AV) technology experiments started in 1920 only and were controlled by radio technology. In 1925, Houdina radio control worked on the radio-controlled autonomous vehicles and tested on New York streets in a dynamic environment. Later in 1926, the chandler motor car company added the transmitting antenna on the vehicle, and it was controlled by one person in another vehicle, then it sends out and receives the radio signals of the following vehicle.

The possibility of developing an AV has been part of the GM Futurama exhibit at the 1939 World’s Fair. In 1939, General Motors equipped with radio-controlled electric cars by electromagnetic fields available of circuits embedded in the sideways of the road. In 1953, Radio Corporation of America successfully launched a prototype car, and it is controlled by weirs and tested in US Route 77 and Nebraska Highway 2.

In the 1960s, Ohio State University developed a driverless car operated by electronic devices and embedded in the roadways. In the 1970s, Bendix Corporation launched and tested the driverless car, controlled by buried cables, operated by roadway communicators, and reposing computer messages. In the 1980s, Mercedes-Benzes robotic van was designed and tested at a speed of 63km/h on the road without traffic. Some universities with the DARPA team and SRI internationals conducted research and developed autonomous vehicles in the same decade. First-time, Lidar, computer vision, and robotics control system technology used and tested at a speed of 31KMPH. Finally, Carnegie Mellon university lunch’s new technology is called a neural network to control the autonomous vehicle.

In 1994 semi-autonomous vehicles developed by Daimler-Benz and Uni-BwM research center developed and tested more than 1000KMPH on a Paris highway in heavy traffic at a speed of 130kmph. In 1995, Carnegie Mellon university’s researchers completed the 98.2% autonomous controlled vehicle. And it is tested almost 5000KMPH traveled across the US country; they called No Hands across America. These researchers use neural network technology to control the steering and remain controlled by a human for safety control.

In 1998 Toyota manufacturer first, introduce an adaptive cruise control system. In 2000, the US government funded unmanned ground vehicles for using military applications to navigate off-line road paths and obstacle avoidance.

Government subsidies and industry consortia across the world initially funded the developments piqued through demonstrations and competitions, then DARPA’s Grand Challenges in 2004 and 2005. The 2007 DARPA Urban Challenge brought the real possibility of autonomous vehicles into the public arena and captured Google executives’ imagination, who later went on to launch their own self-driving car project in 2009.

In 2009, Google started research in self-driving cars privately. Since then, funding and talent have largely shifted from the public to the private sector and have grown rapidly. There was significant progress in technology development and regulatory freedom to undertake tests on roads.

In 2010, major automobile industries started research in autonomous vehicles. Silicon Valley tech giants such as Tesla, Uber, and Waymo, the spin-off from Google, have attracted significant media interest. The 2010s decade saw massive investments in getting from a basic working unit to a robust, high availability, fail-safe, cost-effective product that the market would accept.

Audi first time released and tested a driverless car AudiTTS in a mountain. In 2011, General Motors also released an Electric Networked vehicle. In 2012, Volkswagen created a temporary autopilot (TAP) and tested it on a highway at a speed of 130km/h. In 2013 Toyota created a semi-autonomous vehicle with sensors and communication systems. In 2014, the Mercedes S-class was released in the market with many autonomous options in city and highway traffic at a speed of up to 200KMPH. Tesla Motor also created the first version of the Autopilot Model S with the semi-autonomous vehicle. Later, updated software again released the new model. SAE International published 6 standard levels of an automotive system.

In 2015, Volvo car released level 3 autonomous car but hit the road in 2017. In 2017, Audi also started a new model of A8 with full autonomous options and a speed limit of up to 60kmph using its Audi AI.

The period 2017-2018 signaled a turning point in intensifying development schedules due to a tragic crash. An Uber prototype collided with and killed a pedestrian in Arizona, even though a safety driver was at the driver controls. Additionally, several Tesla drivers crashed and died while using the AutoPilot function. In each case, it appears that either the safety driver or the vehicle owner was not adequately fulfilling their ‘co-pilot’ responsibility to monitor the system and intervene when the system capability was exceeded. This raises questions about shared human-machine control and the implementation of fully automated vehicles that do not rely on human control.

Since 2020, the automobile industry is researching and running trials on level 5 autonomous vehicles. Today, we can see myriad locations where autonomous vehicle technology is being developed and other areas where initial testing is underway. For example:

• In the US, Ford is testing robo-taxis in several cities and launching a limited ADS fleet in 2021 in Miami, Washington DC, and Austin.
• Waymo has a fleet of around 600 AVs in operation, mainly in Phoenix, where it is also working with UPS on local package movement.
• Lyft has provided over 75,000 rides in Las Vegas in partnership with Aptiv as part of the largest US trails to date.
• Walmart and Domino’s Pizza are testing autonomous grocery delivery in Houston in partnership with NURO.
• Peleton is soon launching Level 1 platooned trucks and investing heavily in its Auto-Follower program.
• In Sweden, Einride’s electric autonomous system is being used by Coca Cola to transport goods to food retailer warehouses.

Companies participating in the research journey of autonomous cars since 1920:

1920 – Houdina Radio Control, Chandler Motor Car
1930 – General Motors
1950 – Radio Corporation of America, General Motors Firebird
1960 – Ohio State University, Citroën DS, Bendix Corporation, Stanford University, the Coordinated Science Laboratory University of Illinois at Urbana–Champaign
1980 – Mercedes-Benz, Defence Advanced Research Projects Agency, Carnegie Mellon University, Environmental Research Institute of Michigan, SRI International, HRL Laboratories
1990 – VaMP, Vita-2, Jaguar Cars, Carnegie Mellon University – Navlab, S-Class Mercedes-Benz, Park Shuttle, People mover
2000 – National Institute of Standards and Technology, DARPA, Radio-frequency identification, Royal Academy of Engineering, Toyota, Aluminium division of Rio Tinto, Google.
2010 – General Motors, Ford Motor Company, Mercedes-Benz, Volkswagen, Audi, Nissan, Toyota, BMW, Volvo, Freie Universität Berlin, Karlsruhe Institute of Technology, Infiniti Q50, Google, Tesla, Waymo.
2020 – Waymo, Tesla, Argo AI, GM Cruise, VIT University, and some research and development centers

History of autonomous vehicles – Timeline

1938 – GM Futurama Concept – World’s Fair – New York
1945 – Cruise control invented
1953 – RCA Labs test wire-guided miniature car
1963 – UK TRRL automatic vehicle guidance research project launched
1967 – Remote controlled car tested at Ohio State University
1968 – Vienna Convention on Road Traffic enforces driver control of the car
1977 – First Semi-Automated Vehicle Test – Tsukuba, Japan
1980 – German Bundeswehr tests military robot vehicle
1987 – EU Eureka Prometheus Project launched
1991 – US Congress passes the ISTEA Transportation Authorization bill
1994 – Eureka Prometheus project robotic cars drive 1000km
1995 – Carnegie Mellon first US coast-to-coast autonomous drive 4500km
1995 – Mercedes S Class drives from Munich to Copenhagen using computer vision
1996 – Advanced Cruise-Assist Highway Research Association Demo – Japan
1997 – USDOT Automated Highway System Demo – San Diego, California
1998 – Google founded
1999 – Mobileye founded – Tel Aviv

2000

– Adaptative cruise control launched by Bosch
– Baidu founded

2003 – Tesla Founded
2004 – DARPA Grand Challenge – California
2005 – DARPA Grand Challenge – California
2007 – DARPA Grand Challenge – California
2008 – Rio Tinto launch the Mine of the Future project
2009 – Google Self-Driving Car project launched

2010

– TUB self-driving vehicles demo in Germany
– Uber founded

2011

– Nevada authorizes AV testing
– Peloton truck AV company founded

2012

– Florida authorizes AV testing
– Google completes 300,000 automated driving miles
– Lyft founded as Zimride
– Amazon acquires Kiva Systems for $775m

2013

– FlixMobility founded in Germany
– Port of Rotterdam launches automated guided vehicles
– NuTonomy spun out of MIT
– Caterpillar starts robotics trail
– Google completes 500,000 miles of autonomous driving
– Amazon predicts drone deliveries within 5 years
– Tesla announces Autopilot

2014

– UK Government allows AV testing
– Oxbotica spun out of Oxford University
– Mercedes S Class includes semi-automated features
– Google fully automated prototype tested
– NIO founded in Shanghai

2015

– Apple launches project Titan
– Uber recruits key talent from CMU robotics center
– Tesla Autopilot capability introduced
– Audi, BMW, and Daimler acquire HERE for $3bn from Nokia
– Volvo launches Drive Me project in Sweden

2016

– Volvo pledges that by 2020 no one will be killed in a Volvo
– GM invests $500m in Lyft autonomous vehicle partnership
– GM acquires Cruise Automation for $1bn
– Apple invests $1bn in Chinese rideshare Didi Chuxing
– Ford and VC firms invest in NuTonomy
– Qualcomm acquires NXP for $39bn
– Toyota and Uber announced a partnership
– Uber acquires Otto truck start-up
– Drive.ai spun out of Stanford University
– Uber AV prototypes in San Francisco and Pittsburgh
– Samsung acquires Harman Industries for $8bn
– Pony.ai founded
– US Federal AV policy agreed
– Tesla Autopilot completes 300m miles of operation
– Amazon drone testing in Cambridge, UK
– Waymo spun off as a separate company from Google

2017

– Intel invests in HERE
– Daimler and Nvidia announce AI partnership
– Audi and Nvidia announce AI partnership
– Ford invests $1bn in Argo AI
– Apple starts testing autonomous vehicles
– Intel acquires Mobileye for $15bn
– Bosch and Nvidia announce AI partnership
– Uber completes 2m miles in automated testing
– Peugeot-PSA announces partnership with NuTonomy
– Lyft announces partnership with NuTonomy
– Starsky Robotics truck technology unveiled
– Baidu announces Apollo AV platform and fund
– US Federal AV policy 2.0 agreed
– Ford Lyft partnership announced
– Lyft partners with drive.ai
– Waymo testing without a safety driver
– NuTonomy acquired by Aptiv for $400m
– Tesla semi-truck announced
– Beijing permits AV testing on public roads

2018

– US Federal AV policy 3.0 agreed
– Waymo semi-truck announced
– Self-driving Uber car kills pedestrian
– Baidu completes 140,000 km of self-driving in a year in Beijing
– Volvo launches Vera autonomous platform
– Lyft completes 5,000 self-driving car rides in Las Vegas
– China permits city governments to issue AV road licenses
– Baidu begins mass production of Apollo self-driving bus
– Uber shuts down AV truck project
– Waymo completes 5m miles of testing
– Waymo subsidiary established in Shanghai
– Apollo shuttle bus trial at Shanghai Expo
– California DMV grants permit to Waymo for testing
– Port of Rotterdam tests autonomous navigation

2019

– Tesla driver killed in Autopilot mode
– Tesla ‘Autonomy Day’ announcements
– Rio Tinto starts autonomous truck mining with Caterpillar Inc
– Uber IPO
– Lyft IPO
– Volvo and Uber launch self-driving production car
– Apple acquires Drive.ai
– Amazon announces the launch of drone delivery for Prime
– Toyota partners with Baidu’s Apollo platform
– Baidu completes 1m miles of test driving
– Ford acquires Journey Holding and Quantum Signal AI
– Didi Chuxing spins out self-driving car unit

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LiDAR works by bouncing pulses of lasers off objects and measuring the time the light takes to reflect the sensor. Most self-driving vehicles use roof-mounted LiDARs that rotate, send a pulse of light 360 degrees around the vehicle for identifying nearby objects and create a 3D view of the surroundings. LiDAR plays an indispensable role in autonomous cars as their “seeing eyes.” Without LiDAR, autonomous vehicles would be driving blind.

LiDAR is also an essential sensor used in advanced vehicle driver assist systems (ADAS), such as automated cruise control, collision avoidance systems, and emergency braking systems, offered on many new vehicles today.

However, there are many inherent challenges in LiDAR to overcome in terms of cost, size, and reliability. The current commercial LiDAR units are bulky, sensitive to disruption, and expensive, costing up to $75,000. They use large, rotating mirrors to steer the laser beam and to create a 3-D image. To make LiDAR smaller, more robust, and cheaper, researchers and companies worldwide are working on several prototypes that can go into robots and autonomous vehicles, enabling them to navigate complex environments and dynamically avoid obstacles.

For instance, Volvo is to incorporate its LiDAR-based obstacle detection units from Luminar into some of its cars by 2022. Self-driving technology startup Aurora is developing an in-house LiDAR system “FirstLight” for its driverless vehicles, that can see and track objects quickly and far better than other LiDAR sensors.

Progress in this technology will eventually provide machine vision applications for most robots, dramatically increasing their flexibility, situational awareness, and ability to work in close collaboration with humans. In this post, we will look at some of the crucial breakthroughs and progress achieved by researchers in the development of LiDAR.

1. Leddar Tech

Leddar Tech is developing solid-state LiDAR resistant to mechanical disruption, which can cause significant errors in traditional LiDAR systems. Importantly, their technology provides the same or better levels of sensitivity as other systems that use expensive lasers and mirrors for tasks such as accurate time-of-flight measurements and clear signal-to-noise ratios using inexpensive LEDs. Their hardware and software algorithms permit a high sampling rate and may provide highly efficient machine vision for industrial robots subjected to challenging environments or occasional jostling.

The cameras made by Leddar Tech are relatively small and are more sensitive than traditional LiDAR systems; however, Leddar’s cameras currently have a narrow view. Depending on the application, they would potentially require the use of multiple devices to achieve a sufficiently broad field. These cameras are currently being investigated for use in self-driving cars. Still, they could easily be adapted to a wide array of applications, mainly because of their small size, robustness, and relatively low price point.

2. Photonics Microsystems Group

The Photonics Microsystems Group at MIT is working to dramatically miniaturize LiDAR systems by integrating them onto microchips. These chips can be produced in commercial CMOS foundries on standard 300-millimeter wafers, potentially making their unit production cost about $10. This chip has some limitations, since the current steering range of the beam is about 51 degrees, and it cannot create a 360-degree image by itself. Their chips can only detect objects at 2 meters, but they are working on chips with a range of 100 meters.

Because of their small size and relatively inexpensive manufacturing costs, these chips have the potential to include multiple LiDAR sensors on a single device and expand machine vision applications to even basic consumer-facing robots. Inexpensive 360-degree vision achieved with arrays of these chips for robots would offer safe and effective collision avoidance, responsiveness to human gestures, and more adaptable designs.

3. Takashima Lab

The Takashima Lab at the University of Arizona is another group working on the miniaturization of LiDAR systems. Laser beam steering is a critical component of LiDAR image reconstruction and analysis, which usually contributes significantly to the bulk, expense, and fragility of LiDAR devices. At the SPIE Opto 2018 meeting, J. Rodriguez et al. demonstrated a small and inexpensive 3D-printed LiDAR detection system on a chip.

While some groups are exploring micro-electromechanical systems for LiDAR beam steering, this group has developed a digital micromirror device that is relatively small and provides an improved field of view relative to current LiDAR systems (48 degrees instead of 36 degrees) and a large beam size that is on par with existing LiDAR systems. While the present limitation of this approach is a reduced number of scanning points, the Takashima Lab and others are developing a multi-laser diode detector that may overcome this issue. Overall, this strategy shows some promise, with several devices showing moderate range despite the low cost and the ease of manufacture.

Once developed and available, these chip-based LiDAR systems may be ideal for a suite of short-distance applications such as the detection of nearby obstacles and visually identifying objects to grab or manipulate. For example, robots with these sensors could be used to assemble or disassemble complex machines and classify objects by sight in shipping fulfillment centers, or these chips could be used in miniaturized pipeline inspection robots.

4. Gopinath Lab

Liquid lens-based autofocusing of light facilitates robust real-time control of the light used in LiDAR sensing. Gopinath Lab at the Colorado University explores the concept, using a weak electromagnetic current to manipulate the shape of several lenses. This technology is currently commercially available for other applications. It is being sold by companies such as Cognex, which provides off-the-shelf tunable liquid lenses for directing and concentrating lasers.

These lens systems are mechanically robust as they do not require the movement of physical parts to direct the laser path. The system is also relatively inexpensive for new application development, as it is already in production. These factors will potentially make this technology ideal for LiDAR applications, particularly in cases where the robot must be able to rapidly change the focus of the objective.

5. University of Colorado Boulder

Researchers at the University of Colorado Boulder made a breakthrough in LiDAR technology by creating a new silicon chip with no moving parts or electronics that improve the resolution and scanning speed needed for a LiDAR system. For three years, the team has been working on a way of steering laser beams, called wavelength steering, in which each wavelength or color is pointed to a unique angle to create 3D images.

According to a paper titled “Serpentine optical phased arrays for scalable integrated photonic LiDAR beam steering, published in the journal Optica, they’ve not only developed a way to do a version of this along two dimensions simultaneously, instead of only one, but they’ve also done it with color, using a “rainbow” pattern to take 3-D images. Since the beams are easily controlled by simply changing colors, multiple phased arrays can be controlled simultaneously to create a bigger aperture and a higher resolution image. The simpler and smaller these silicon chips are while retaining high resolution and accuracy, the more technologies they can be applied to, including self-driving cars and smartphones.

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