LiDAR has served as a useful tool in many industries for decades, but only recently are we starting to realize its true potential with the introduction of artificial intelligence (AI)-powered solutions. LiDAR, also known as light detection and ranging, is a remote sensing technology. It uses laser scanners to measure distances and dimensions between the sensor and target object, such as a building or pedestrian. With AI used in tandem with LiDAR, teams are optimizing the technology for unimaginable speed and precision across a variety of use cases.
LiDAR has been around in some form since the 1960s, when it was first installed on planes to scan terrain. With the introduction of GPS (Global Positioning System) in the 1980s, LiDAR grew more popular, as GPS enabled the data collected from LiDAR scans to be used for building 3D models. Today, as costs associated with LiDAR are decreasing and the breadth of LiDAR data available increases, its recent pairing with AI and machine learning (ML) is unlocking major opportunities to innovate.
How LiDAR Works
A LiDAR system generally consists of four key elements:
Laser: Sends pulses of light to target objects (could be buildings, vehicles, or pedestrians). The light waves are typically ultraviolet, visible, or near-infrared; the type used will depend on the type of LiDAR employed.
Scanner: Regulates the speed at which the laser scans target objects and the distance the laser can reach.
Sensor: Measures the length of time it takes for the light to bounce off the target object and return to the LiDAR system.
GPS: Tracks the location of the LiDAR system to ensure distance measurements between the target object and system are precise.
A modern LiDAR system can send 500,000 pulses every second. The system aggregates these pulses into a point cloud, which is a dataset of coordinates that represents objects in space. The point cloud is then used to create a 3D model of the space.
There are two main categories of LiDAR:
As the name suggests, airborne LiDAR requires the system to be installed on a flying apparatus, like a drone or plane. In this case, the LiDAR sends pulses to the ground to monitor relevant conditions. There are two types of aerial LiDAR:
Bathymetric: Uses green light to penetrate water bodies and measure their depth.
Topographic: Used for mapping the surface of the land.
The LiDAR system is installed on a moving vehicle or tripod fixed to the ground. It can scan in all directions and is used to create 3D models out of point clouds. There are two types:
Mobile: The LiDAR is installed on a train, boat, or automobile. It works well for observing roads, pedestrians, signs, conditions, and other relevant infrastructure.
Static: The LiDAR is fixed to a point in the ground and scans the surrounding area or a specific feature, such as a building interior.
Using LiDAR with AI
The relationship between LiDAR and AI is a natural fit: LiDAR collects 3D points to create a point cloud, and AI thrives on processing data. LiDAR pulse rates typically range from 10,000 to 200,000 pulse per second and can generate multiple returns from the same laser pulse. The output returns can be processed by AI models to make sense of a given environment (like creating topographical maps).
In the past, teams manually labeled the data generated by LiDAR to identify key objects in the scan. Unsurprisingly, the effort was both laborious and time-consuming and required very specialized expertise. Thanks to advances in computer vision and image processing, AI now helps automate the labeling process. Today, it’s able to process unstructured data input and accurately output target objects (for example, nearby vehicles or infrastructure) for further analysis.
With the time-savings AI allots, we have the power to develop 3D models of our world that are both highly precise and consistently up-to-date. Given these advances, there are now numerous applications of it and AI that span across major industries.
It is essential to many industries: architecture, manufacturing, oceanography, 3D printing, virtual reality, and more. Here are a few highlighted examples of its application in AI:
Self-driving cars may not be the norm on our roadways yet, but that day will soon come. These vehicles require AI-powered LiDAR to scan the surrounding area, create 3D models of the space, and make nearly instantaneous decisions on how the vehicle should proceed, in combination with data input from RADAR and cameras. Accurate LiDAR is critical in ensuring the safety of the vehicle’s passengers.
Check out our AI Solutions to Automotive eBook to learn about customer-centric AI for autonomous vehicles and smart cars.
In agriculture, teams use AI-powered LiDAR systems installed on drones to quickly create topographical maps of fields. These maps help farmers determine ideal areas to grow crops, apply fertilizer, and apply pesticides based on elevation and sun exposure. Post-seed dispersion, farmers can use it to track crop yield rates as well.
Safety in Military, Construction, and More
Many militaries have used LiDAR for decades to scout borders and identify suspicious objects. With more opportunities opened by AI, we may see autonomous surveying of environments for potential danger. Autonomous robots may also help to protect workers on hazardous job sites in construction or related fields.
The partnership between AI and LiDAR continues to grow, in many ways due to advances in computer processing power and the allocation of more resources to invest in AI opportunities. With it’s existing prevalence across numerous industries, innovation is sure to come to many facets of our lives, shaping the way we experience technology as we move toward an AI-powered world.
Insight From Kuo-Chin Lien, an Appen Expert on LiDAR
At Appen, we rely on our team of experts to help you build cutting-edge models utilizing LiDAR. Kuo-Chin Lien, the Director of Data Science at Appen, works to ensure Appen customer models using LiDAR are executed successfully. Kuo-Chin’s top insights include:
Spatial Computing Applications: from Smart Cars to Smart Phones
Leveraging LiDAR enables access to the full 3D information of scanned objects and their environment. This creates the opportunity for numerous applications, especially when spatial information e.g., depth, distance, geometry, and dimension matter for your use case. For example, in retail, it can be inventory management; or in construction, it can be quality assurance and comparing delivered work with approved plans. Another great example of a use case that has gained a lot of traction this year is with car companies that have started using it to detect objects on the road, thus improving their ADAS (Advanced Driver-Assistance System) applications; Even smartphones use LiDAR -iPhone 12 Pro is equipped with LiDAR to help it separate foreground from distant background, and provide more focused photos in portrait mode. And this is just the tip of the iceberg when it comes to it’s applications across a diverse set of industries.
3D Deep Learning to empower spatial computing
Experts across industries rely on Deep Learning(DL) techniques to make sense of 3D scanned data. Without it, it’s nearly impossible to make sense of all the data points, and AI gives you the super-human capability to understand 2D image content at scale. When comparing DL solutions in one dimension, like for speech recognition, or in 2D, for image signals, DL algorithms can maintain real time performance and low memory footprint with additional dimensions coming in. Experts are looking to leverage sparsity of the irregular data while maintaining precision, which is not an easy task. How can they engineer an AI that produces reliable reasoning instantaneously from imperfect scans and power all these exciting new use cases? These are all interesting topics to continue to explore and could further increase the usability of DL models for LiDAR. To learn how these problems are being approached in industry and in academia, join us for the June 2021 IEEE workshop in the summer, to hear from Kuo-Chin and other industry experts.
What Appen Can Do For You
We provide data annotation and collection services founded on over 25 years of experience. Our intelligent annotation platform offers cutting-edge LiDAR, PLSS, and computer vision ML-assisted tools. Our multimodal computer vision ML-assisted tools help you launch world-class AI, whether your project is a self-driving car or something else. Our platform and services enable high precision and speed, so you can scale quickly, knowing you’re equipped with large volumes of high-quality, annotated data.
Learn more about our platform or contact us today to speak with someone directly.