TheĪpproach is evaluated qualitatively and quantitatively with real-world dataįrom a moving vehicle in urban environments. For comparison, also a lidar-based method is developed. Subsequently, the clustering of dynamic areas provides high-level object Therefore in this work, the data of multiple radar sensorsĪre fused, and a grid-based object tracking and mapping method is applied. Publication about dynamic occupancy grid mapping with subsequent analysis based To the best of the author's knowledge, there is no Our method uses the pose estimation from the SLAM system, its sparse map. We extend this mapping stage to build an occupancy grid map given the sparse point cloud. This paper presents the further development of a Feature-based SLAM is efficient, fast, and can offer an accurate localization system on the other hand, the map produced is a sparse representation of the environment, limiting path planning activities and reducing robotic autonomy. Grid map approach, which assumes a static environment, has been extended toĭynamic occupancy grid maps, which maintain the possibility of a low-level dataįusion while also estimating the position and velocity distribution of theĭynamic local environment. Download a PDF of the paper titled Radar-based Dynamic Occupancy Grid Mapping and Object Detection, by Christopher Diehl and 5 other authors Download PDF Abstract: Environment modeling utilizing sensor data fusion and object tracking isĬrucial for safe automated driving.
