LiDAR, an acronym for “light detection and ranging,” is a remote-sensing technology that uses laser beams to measure precise distances and movement in an environment, in real time.
LiDAR data can be used to generate everything from detailed topographic maps to the precise, dynamic 3D models that are required to safely guide an autonomous vehicle through a rapidly and constantly changing environment. LiDAR technology is also used to assess hazards and natural disasters such as lava flows, landslides, tsunamis and floods.
Find value by balancing LiDAR and satellite imagery for vegetation maintenance.
Register for the playbook on smarter asset management
LiDAR works on the same principles as radar ("radio detection and ranging," a location system that is often used by ships and planes) and sonar ("sonic navigation and ranging," a system that is typically used by submarines). All three technologies emit waves of energy to detect and track objects. The difference is that while radar uses microwaves and sonar uses sound waves, LiDAR uses reflected light, which can measure distance faster, with greater precision and higher resolution than either radar or sonar.
A typical LiDAR instrument is made up of several components:
- a laser scanner that emits rapid pulses of near-infrared laser light
- a LiDAR sensor that is used for detecting and collecting the returning light pulses, and
- a processor for calculating the time and distance and for building the resultant data set, called a LiDAR point cloud.
For the remote sensing to be accurate, measurements of time and space must be exact, so a LiDAR system will also use time-keeping electronics, an inertial measurement unit (IMU) and GPS.
The LiDAR instrument emits pulsed laser light into the environment. These pulses, traveling at the speed of light, bounce off surrounding objects and return to the LiDAR sensor. The sensor measures the time that it took for each pulse to return and calculates the distance that it traveled. Because the speed of laser light is constant, this “time of flight” can be used to calculate precise distances.
By repeating the process and sending out laser pulses across a larger area, time-of-flight measurements can be collected on billions of individual points and processed in real time into what is known as a LiDAR point cloud.
The data undergoes several processing stages to transform the LiDAR point cloud into a 3D map. First, it is checked for correctness and completeness and cleaned to remove anomalous noise. Then, ground surface features like buildings, riverbanks and forest canopy can be algorithmically identified and classified.
To simplify the analysis, algorithms downsample the point cloud to remove redundant data and reduce file size. The data is then converted into the industry-standard LAS (or LASer) file format used for exchanging 3D x, y, z data.
Finally, once converted into LAS, the point cloud data can be visualized and modeled into a 3D map of the scanned terrain. These computations are constant and ongoing for a moving LiDAR system such as those used in autonomous vehicles. According to one source, self-driving cars generate and process a terabyte of data every hour of operation.1
LiDAR systems can be divided into two main types based on their platform: airborne LiDAR and terrestrial LiDAR.
Airborne LiDAR systems, also called airborne laser scanning systems, use LiDAR scanners mounted to aircraft (usually helicopters or UAVs) to generate 3D models of the ground surface.
Airborne LiDAR mapping has become a valuable tool for creating digital elevation models of the Earth’s surface, mainly replacing the older and less accurate photogrammetry method. Airborne LiDAR scanning is also used extensively in forestry to build LiDAR surveys of the forest canopy and topographic terrain models of the forest's ground surface.
Types of airborne LiDAR technology include:
Bathymetric LiDAR
Bathymetric LiDAR captures GIS data in shallow water and along coastlines. Bathymetric LiDAR emits green laser beams at a wavelength that can penetrate water to measure the digital elevation of the seafloor, instead of using infrared laser light like typical LiDAR systems.
Space-based LiDAR
NASA and other space agencies use space-based LiDAR for spacecraft navigation and digital mapping of celestial bodies. LiDAR is also used to pilot NASA's autonomous vehicles and fly the helicopter Ingenuity on Mars.
Terrestrial LiDAR is a ground-based LiDAR system frequently used for terrain and landscape mapping. Terrestrial LiDAR can be used to collect more localized and short-range data, making it ideal for mapping smaller areas with high precision.
Types of terrestrial LiDAR include:
Static LiDAR
Some terrestrial LiDAR systems are static, fixed in one location, and used to take precise and repeated LiDAR scans of a single area. Static LiDAR is often used for archeological sites, construction projects and hazard assessments. It can monitor the ground surface of active volcanoes, earthquake faults and flood zones.
Mobile LiDAR
Mobile LiDAR is a form of terrestrial LiDAR that collects LiDAR data from a moving vehicle. Mobile LiDAR systems (MLSs) are instrumental to the automotive industry in developing driver assistance and autonomous driving: the data collection from real-time light detection and ranging allows self-driving cars to identify roadway assets and infrastructure quickly, accurately and cost-efficiently.
LiDAR scans have a wide range of real-world uses across many industries. They can create detailed terrain models of ground surface and seafloor and also produce precise, high-resolution, real-time visualizations of moving objects.
LiDAR sensors are used to measure agricultural landscaping and topography, for crop biomass estimation, and to detect soil properties by mapping variations in depth, slope, moisture and aspect. LiDAR is also used to pilot autonomous farm vehicles.
LiDAR is used for terrain mapping, target tracking, mine hunting and imaging through clouds, and mission planning using sophisticated battlefield visualizations even in dense urban environments.
Learn more about defense and intelligence technology solutions
Advanced driver-assistance systems (ADAS) and autonomous vehicles like self-driving cars use 3D LiDAR map data to "see" and navigate roads and other environments.
LiDAR can be used for accurate measurements of wind speed, and is also used by airports to track aircraft and foreign object debris (FOD).
Bathymetric LiDAR uses green laser light to penetrate water and create digital elevation models of shallow water reservoirs, rivers and coastal sea floors. These can measure erosion, map wildlife habitat and assess risk inside flood zones.
Learn how AI, IoT and digital monitoring can transform seaports and shipping
LiDAR can quickly and accurately survey a construction site, calculate the volume of materials and be used to perform safety inspections and detect possible hazards.
Learn how a digitally integrated approach empowers building projects
LiDAR technology is used for wind resource assessment, oil and gas exploration, and vegetation management for the maintenance of power lines.
LiDAR is used for mapping environments in virtual reality and augmented reality applications.
In addition to providing detailed topographic maps, LiDAR can be used to measure the structural characteristics of trees such as leaf area index and forest canopy volume, and is a valuable tool in vegetation management. It is also used to monitor and contain forest fires.
Learn how technology solutions help halt global deforestation
Mines and quarries are difficult to access, and LiDAR is increasingly used for surveying, mapping and worker safety. LiDAR scans can also be used for volume measurements in quarries.
LiDAR technology can be used to create 3D models of objects for use in manufacturing. It can also be used for quality control to detect anomalies and defects.
LiDAR is used to create digital elevation models and map roads, bridges and other geographic and infrastructure features.
Learn how geospatial data can be used to provide business insights
LiDAR can be used to scan through the forest canopy and monitor vegetation density, species and health to identify vegetation that might be high risk to utilities and other infrastructure.
LiDAR sensors are used to measure temperature, cloud cover, wind velocity, air density and other atmospheric parameters, and provide vital data for weather forecasting models.
Research teams are continuously developing new systems and algorithms to increase LiDAR's accuracy, speed and effectiveness, and there is ongoing development focused on making LiDAR technology smaller, lighter and more affordable. This would enable broader adoption across various industries and applications, including consumer electronics, robotics and smart home devices. LiDAR is becoming increasingly popular in autonomous vehicles and is expected to play a significant role in the future of automobiles.
As technology continues to improve and costs decrease, the applications of LiDAR are likely to increase dramatically.
Gain climate and weather insights to anticipate disruptive environmental conditions, proactively manage risk and build more sustainable operations.
Turn sustainability ambition into action and act on your sustainability goals for a resilient future with IBM’s sustainable solutions.
Use satellite data and Geiger-mode LiDAR predictive analytics that are combined with weather insights to deliver artificial intelligence-driven insights and make more informed decisions about utility vegetation management.
Learn how geospatial data has become an essential tool for strategic business planning around environmental sustainability.
Discover how industrial businesses are integrating operational technology (OT) with IT data to improve asset maintenance, monitoring and management.
Make better vegetation management decisions by combining weather, satellite and IoT data with intelligent prioritization and reporting capabilities.
See how organizations use geospatial data to reveal patterns and trends, and gain insights into relationships between variables.
Footnotes
1David Edwards, "On the Way to Solving the Big Data Problem in Autonomous Driving"(link resides outside of ibm.com), Robotics and Automation, July 21, 2022.