2023/03/20 by Max Peter Ronecker, Ronecker, Max Peter, Michael Stolz +3
Computer Science · Engineering · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Robotics (cs.RO) #Target Tracking and Data Fusion in Sensor Networks #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2303.11390
openalex publication_date 2023/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Through constant improvements in recent years radar sensors have become a viable alternative to lidar as the main distancing sensor of an autonomous vehicle. Although robust and with the possibility to directly measure the radial velocity, it brings it's own set of challenges, for which existing algorithms need to be adapted. One core algorithm of a perception system is dynamic occupancy grid mapping, which has traditionally relied on lidar. In this paper we present a dual-weight particle filter as an extension for a Bayesian occupancy grid mapping framework to allow to operate it with radar as its main sensors. It uses two separate particle weights that are computed differently to compensate that a radial velocity measurement in many situations is not able to capture the actual velocity of an object. We evaluate the method extensively with simulated data and show the advantages over existing single weight solutions.