Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera Geometry

• Published in: IEEE transactions on robotics Vol. 40; pp. 2254 - 2271
• Main Authors: Chen, Yuyang, Wang, Dingkang, Thomas, Lenworth, Dantu, Karthik, Koppal, Sanjeev J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active perception; Cameras; Field of view; Hardware; Image stabilizers; Inertial platforms; Laser radar; Lidar; Micro air vehicles (MAV); Micromechanical devices; Microrobots; Motion compensation; Odometers; Perception; Robot dynamics; Robot sensing systems; robot vision systems; Robots; Sensors; Simultaneous localization and mapping; Stabilization; Unmanned aerial vehicles; Vibration; vision sensors; Wind effects
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2024.3371885

A fundamental challenge in robot perception is the coupling of the sensor pose and robot pose. This has led to research in active vision where the robot pose is changed to reorient the sensor to areas of interest for perception. Furthermore, egomotion, such as jitter, and external effects, such as wind and others, affect perception requiring additional efforts in software such as image stabilization. This effect is particularly pronounced in microair vehicles and microrobots that typically are lighter and subject to larger jitter but do not have the computational capability to perform stabilization in real time. We present a novel microelectromechanical mirror light detection and ranging (LiDAR) system to change the field of view of the LiDAR independent of the robot motion. Our design has the potential for use on small, low-power systems where the expensive components of the LiDAR can be placed external to the small robot. We show the utility of our approach in simulation and on prototype hardware mounted on a unmanned aerial vehicle (UAV). We believe that this LiDAR and its compact movable scanning design provide mechanisms to decouple robot and sensor geometry allowing us to simplify robot perception. We also demonstrate examples of motion compensation using inertial measurement unit (IMU) and external odometry feedback in hardware.