LiDAR

Definition

LiDAR (Light Detection and Ranging) is a remote sensing technology that measures distances by emitting laser pulses and detecting the reflected light. In robotics, LiDAR sensors generate 2D or 3D point clouds of the surrounding environment, enabling obstacle detection, mapping, and localization.

Formula

d = \frac{c \times t}{2}

In-Depth Explanation

LiDAR works by emitting rapid pulses of laser light (typically near-infrared at 905 nm or 1550 nm) and measuring the time for each pulse to return after reflecting off a surface. This time-of-flight measurement is converted to a distance: d = (c × t) / 2 where c is the speed of light and t is the round-trip travel time. By sweeping the laser across angles (rotating or solid-state), LiDAR builds a spatial map of surrounding surfaces as a point cloud — a set of 3D coordinates (x, y, z) with optional intensity values. Types of LiDAR: 1. Mechanical (spinning) LiDAR: - Rotating mirror/sensor array covers 360° - Examples: Velodyne VLP-16 (16 beams), HDL-64E (64 beams) - Used in most early autonomous vehicles and robots 2. Solid-state LiDAR: - No moving parts — uses MEMS mirrors, OPA (Optical Phased Array), or Flash LiDAR - More durable, compact, and cost-effective - Examples: Livox Mid-360, Ouster OS0 3. 2D LiDAR (laser scanner): - Single plane scan (horizontal) - Common in indoor mobile robots (e.g., SICK TiM, Hokuyo URG) - Output: polar scan data → used directly in 2D SLAM Key specifications: - Range: 10 m (indoor) to 200+ m (automotive) - Angular resolution: 0.1° to 0.4° (horizontal) - Scan rate: 10–20 Hz for most spinning LiDARs - Number of channels/beams: 1 (2D), 16, 32, 64, 128 - Point rate: Up to 2.4 million points/second Robotics applications: - 2D navigation and obstacle avoidance - 3D SLAM and environment reconstruction - Object detection and classification - Autonomous vehicle perception - Drone altitude estimation and collision avoidance Practical example: An autonomous delivery robot uses a 2D LiDAR (Hokuyo URG-04LX) scanning at 10 Hz. Each scan returns 682 distance measurements across a 240° field of view. These scans are fed into a SLAM algorithm (GMapping) to build a map of the corridor and localize the robot within it. LiDAR vs Camera comparison: - LiDAR provides accurate depth (±2 cm) regardless of lighting conditions - Cameras provide rich texture and color but require algorithms (stereo, monocular depth) to estimate depth - Modern systems fuse both (LiDAR-camera fusion) for robust perception

Related Terms

ROS (Robot Operating System)

ROS (Robot Operating System) is an open-source middleware framework for robot software development. Despite its name, ROS is not a traditional operating system — it provides tools, libraries, and conventions that simplify the creation of complex and reusable robot software across a wide variety of robotic platforms.

Sensor Fusion

Sensor fusion is the process of combining data from multiple sensors to produce a more accurate, consistent, and reliable estimate of a system's state than any single sensor could provide alone. In robotics, it is essential for tasks like localization, navigation, and perception, where individual sensors have complementary strengths and weaknesses.

SLAM (Simultaneous Localization and Mapping)

SLAM (Simultaneous Localization and Mapping) is the computational problem of constructing or updating a map of an unknown environment while simultaneously tracking a robot's location within it. It is a foundational capability for autonomous mobile robots operating without GPS or pre-built maps.