Abstract
This paper presents a method for a tracking (estimation of position, velocity and size) of moving objects in global navigation satellite system (GNSS)-denied environments using a light detection and ranging (LiDAR) mounted on a smart helmet that is worn by a rider of micro mobility. Distortion in the scanning LiDAR data, which is caused by pose changes of micro mobility and rider, is corrected by estimating the pose (3D positions and attitude angles) of the smart helmet in a period shorter than the LiDAR scan period using normal distributions transform-based simultaneous localization and mapping (NDT SLAM) and the information from an inertial measurement unit (IMU) via the extended Kalman filter. Subsequently, the scan data of interest are extracted by subtracting the local environment map generated by NDT 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 a university campus environment demonstrate the tracking performance of the proposed method.
