LiDAR

LiDAR (Light Detection and Ranging) measures distances by emitting laser pulses and timing their reflections, generating precise 3D point clouds of the surrounding environment. It's a foundational sensing technology for autonomous vehicles, robotics, spatial computing, and geospatial mapping.

The basic principle: emit a laser pulse, measure the time until the reflected signal returns, and calculate distance from the speed of light. By scanning thousands to millions of pulses per second across a scene, LiDAR builds a dense 3D point cloud with centimeter or millimeter accuracy. Unlike camera-based depth estimation (which infers depth from visual cues), LiDAR provides direct geometric measurement regardless of lighting conditions or surface texture.

LiDAR comes in several architectures. Mechanical spinning LiDAR (like Velodyne's units) rotates a laser array 360°, providing surround-view point clouds. Solid-state LiDAR eliminates moving parts using optical phased arrays, MEMS mirrors, or flash illumination, enabling smaller, cheaper, more reliable sensors. FMCW (frequency-modulated continuous wave) LiDAR measures both distance and velocity simultaneously by analyzing frequency shifts, providing richer data for motion planning.

In consumer devices, Apple introduced LiDAR to iPad Pro (2020) and iPhone Pro models, using a direct time-of-flight (dToF) sensor that produces per-pixel depth maps. This consumer LiDAR enables room scanning, AR object placement, and improved photogrammetric reconstruction. Apple Vision Pro uses LiDAR as part of its passthrough depth sensing system.

For autonomous vehicles, LiDAR provides the 3D environmental awareness needed for safe navigation. Waymo, Cruise, and most autonomous driving programs rely on LiDAR as a primary sensor (Tesla being the notable exception, betting on camera-only approaches). The cost trajectory has been dramatic: from $75,000+ per unit in 2012 to under $500 for automotive-grade solid-state units, with companies like Hesai, Luminar, and Ouster driving commoditization.

LiDAR data feeds into SLAM (Simultaneous Localization and Mapping) algorithms that allow robots and vehicles to build maps of unknown environments while tracking their own position within them. Combined with computer vision and AI-based scene understanding, LiDAR-equipped systems can navigate complex environments, detect obstacles, and create detailed digital twins of physical spaces.

The technology is also transforming large-scale mapping and environmental monitoring. Aerial LiDAR (from aircraft and drones) creates terrain maps that penetrate forest canopy, revealing archaeological sites, measuring flood risk, and monitoring deforestation. The combination of drone-mounted LiDAR with AI analysis is enabling environmental monitoring at unprecedented scale and resolution.