Apple's latest Pro and Pro Max series feature a LiDAR scanner, allowing for improved augmented reality (AR) experiences, night mode portraits, and faster autofocus in low light.
For most consumers, this is their first experience with LiDAR. But the technology has been around for more than 60 years. Hughes Aircraft Company built the first LiDAR prototype in 1961. The United States space program was among the first to use the technology to map the moon during the 1971 Apollo 15 mission.
Today, LiDAR is more down to earth. It’s becoming common in computer vision—to train autonomous vehicles, inspect power lines, pinpoint crops for fertilization, and much more.
What is LiDAR?
LiDAR, or Light Detection and Ranging, is an active remote sensing technology that uses light in the form of a pulsed laser to measure distance. Light pulses, combined with other data points generated by the system, create accurate 3-D information about an object in relation to its environment. LiDAR is unparalleled in depth and dimension for finding the distance between objects and does so faster than RADAR or cameras.
LiDAR can enable visibility through dense environments, such as forest canopies. It can create high-resolution digital elevation models with vertical accuracy of up to 1 centimeter. A LiDAR device has several components: a laser scanner, a GPS, and an Inertial Navigation System (INS). The equipment typically mounts onto a mobile vehicle, such as an automobile, drone, or UAV.
Types of LiDAR
Functionally, LiDAR systems are either airborne or terrestrial. Here’s a brief look at each.
Airborne LiDAR is placed on a drone or helicopter and is helpful for applications that require a bird’s eye view of a vast area. Here, two types of standard LiDAR. The first, topographic, uses a near-infrared laser to map land areas. The second, bathymetric, uses a green water-penetrating light to map underwater terrain.
Terrestrial LiDAR works on the ground and is either mobile or static. Mobile LiDAR systems mount on moving platforms such as autonomous vehicle AI applications to identify objects in the driving environment. Unlike mobile, static LiDAR systems are installed on stationary structures such as tripods—this type of LiDAR is prevalent in archeology, surveying, mining, and engineering.
LiDAR data is accurate, fast, and beneficial for any location where the structure and shape of objects must be determined.
7 Interesting LiDAR Applications
LiDAR is a valuable technology for several industries, from autonomous vehicles to surveying.
Below are seven interesting applications of LiDAR:
Autonomous VehiclesFor self-driving vehicle applications, LiDAR provides a longer-range alternative to still image and video cameras, which aren’t as effective in poor atmospheric conditions like rain and fog. Vehicles fitted with LiDAR systems collect data such as road markings, traffic signs, pedestrians, road obstructions, and other vehicles. LiDAR is typically used with vision-based systems for fully autonomous (Level 5) vehicles.
Aerial InspectionDrones/UAV LiDAR data provides valuable aerial insight into industrial assets that are difficult to inspect, including power lines, civil infrastructure, and other industrial assets, to reduce operational maintenance costs.
Precision AgricultureLiDAR can help agriculture technology (agtech) companies pinpoint areas to optimize water, fertilizer, and herbicides or manage pest control to improve crop yield.
Forestry and Land ManagementLiDAR can be used to measure the vertical structures and density of the canopy in forests. This data can then be analyzed for environmental impact, land management, and fire prevention planning.
Survey and mappingLiDAR creates accurate maps and digital elevation models for geographic information systems (GIS) to aid civil and commercial surveying and mapping applications.
Renewable EnergyLiDAR can identify requirements for harnessing solar and wind energy, such as optimal solar panel positioning. It can calculate direction and wind speed to allow the operators of wind farms to build and place turbines.
RoboticsLiDAR is used to equip robots with mapping and navigation capabilities. The technology trains an autonomous system to recognize the distance between the vehicle and other objects in the environment.
Making LiDAR Data Useful for Computer Vision
To be useful for computer vision, and more specifically, supervised machine learning, LiDAR data must be accurately labeled, which is a big job that can be difficult to scale. The challenge for AI developers is transforming massive, raw data into large amounts of structured data that can be used to train machine learning models. That requires hours and hours of labeling data to prepare for training machines to interpret and understand the visual world.
At CloudFactory, we understand that annotating data for computer vision models requires a strategic combination of people, process, and technology. It’s our specialty. If your organization works with LiDAR technology, our professionally managed teams of data analysts can help. Contact us to learn how we can annotate data for computer vision use cases with high accuracy using virtually any tool.