Our Research Paper on 3D Perception for Robotics has been accepted by IEEE Transactions on Instrumentation and Measurement

Ming Cao, Pengpeng Su, Haoyao Chen*, Shiyu Tang, and Yunhui Liu, 3D Dense Rangefinder Sensor With A Low Cost Scanning Mechanism, IEEE Transactions on Instrumentation & Measurement, accepted, 2020.

Abstract: LiDAR sensors have been widely applied in autonomous robotics and autonomous systems. High-channel LiDARs or multiple low-channel LiDARs are adopted in these applications to overcome the poor vertical resolution of point clouds, as this scenario can lead to high costs. Here, as a means to improve the vertical resolution of point clouds and lower the cost, we present a 3D dense rangefinder sensor composed of a low-channel LiDAR, a camera, a brush-less motor, and a crank–link system to replace the traditional LiDAR. A special registration method is designed to register the high-dynamic point cloud. The measurement uncertainty of this method is analyzed. In addition, a 3D object detection method is used to obtain the 3D pose of obstacles by combining the dense point cloud and an image-based 2D object detection algorithm. Finally, several experiments are performed to evaluate the effectiveness of the proposed 3D rangefinder sensor.