Moving-object detection and tracking by scanning LiDAR mounted on motorcycle based on dynamic background subtraction

Abstract

This paper presents a method for moving-object detection and tracking (DATMO) in global navigation satellite systems (GNSS)-denied environments using a light detection and ranging (LiDAR) mounted on a motorcycle. Distortion in the scanning LiDAR data is corrected by estimating the pose (3D positions and attitude angles) of the motorcycle in a period shorter than the LiDAR scan period using normal distributions transform-based simultaneous localization and mapping (NDT-based SLAM) and the information from an inertial measurement unit (IMU) via the extended Kalman filter (EKF). The scan data of interest are extracted by subtracting the local environment map generated by NDT-based SLAM from the LiDAR scan data. Moving objects are detected from the scan data of interest using an occupancy grid method and are tracked with a Bayesian filter. Experimental results obtained from public road and university campus environments demonstrate the effectiveness of the proposed method.