Ingredient
LiDAR rangefinder
Also known as: TF-Luna, VL53L1X, RPLiDAR, Time-of-flight sensor
Time-of-flight laser-based distance sensor — emits a laser pulse, measures the time-of-return, computes distance. The right ingredient when an autonomous machine needs to perceive its physical environment for obstacle avoidance, navigation, or canopy mapping. Two classes: single-point sensors ($10–30, 1D distance, e.g. VL53L1X, TF-Luna) and 2D scanning LiDAR ($60–500, 360° plane, e.g. RPLiDAR A1/A2, YDLiDAR). The sensor that makes a Raspberry Pi or Jetson into a navigating robot.
Inputs / outputs
- Single-point ToF (VL53L1X, TF-Luna): I²C or UART, 4 m–8 m range, 30–50 Hz, ±5 mm accuracy
- 2D scanning LiDAR (RPLiDAR A1): USB serial, 360° plane, 12 m range, 8 kHz sampling, 5.5 Hz rotation
- Power: ~100–500 mA depending on model
- Output: distance arrays — single-point sensors output one number, scanning LiDAR outputs an angle-distance array per rotation
Solves / unlocks
- Autonomous-rover navigation (combined with IMU and wheel odometry → SLAM)
- Crop-row following (detect parallel row walls; correct heading)
- Obstacle avoidance (stop or replan when distance < threshold)
- Canopy height profiling (drone-mounted; estimate biomass)
- Grain-bin level monitoring (single-point in the bin headspace)
Constraints
- Outdoor sun saturates many cheap ToF sensors — bright direct sunlight overwhelms the receiver; use 905 nm sensors or shaded mounts.
- Reflective surfaces fool LiDAR — wet leaves, water, glass return weak or specular signals.
- Dust and fog scatter the laser; performance degrades in field conditions.
- 2D-only is insufficient for full obstacle awareness — pair with [[rgbd-camera|depth camera]] for 3D.
Source
- VL53L1X (single-point): https://www.st.com/en/imaging-and-photonics-solutions/vl53l1x.html
- TF-Luna (single-point UART): https://www.benewake.com/en/tfluna.html
- RPLiDAR (2D scanning): https://www.slamtec.com/en/Lidar/A1
- ROS drivers: https://github.com/Slamtec/rplidar_ros
See also
Auto-generated from this entry’s typed relations: frontmatter, grouped by relation type so the editorial signal isn’t flattened.
- Member of: [[ingredient]]
- Combines with: [[ros2]] · [[nvidia-jetson]] · [[raspberry-pi]] · [[rgbd-camera]] · [[acorn-rover]]
What links here, and how
Inbound connections from across the wiki, grouped by lens and by relationship. These appear automatically — every entity page declares what it links to, and that data populates here on the targets.
Practical
combines with
- Acorn rover Acorn uses scanning LiDAR for in-field navigation
- NVIDIA Jetson LiDAR + Jetson = obstacle-avoidance and crop-row tracking for autonomous machines
- RGB-D camera complementary perception: depth-camera for 3D structure, LiDAR for accurate distance
- ROS 2 ROS2 has drivers for every common LiDAR; standard input to Nav2
contains
- Farm-tech toolkit sensor / time-of-flight distance for navigation
combines
- Recipe: autonomous row-crop weeder obstacle avoidance — people, animals, equipment in the row
6 inbound links · 6 outbound