부산대학교 도서관

Dynamic object detection from point clouds has been widely studied in recent years to achieve accurate and robust LiDAR odometry for autonomous driving. Satisfactory accuracy can be achieved by dynamic object detection from point clouds has been widely studied in recent years to achieve accurate and robust LiDAR odometry for autonomous driving. Satisfactory accuracy can be achieved by detecting and removing the object points in the urban environment. However, it is still not clear how dynamic objects numerically affect the performance of LiDAR odometry. In addition, the existing solutions tended to directly remove the LiDAR features belonging to the dynamic object, which can lead to the degradation of the geometry constraints of the surrounding features. This paper aims to give answers to these problems by evaluating the effects of dynamic objects as well as reweighting both dynamic objects and static objects. Three factors affecting the performance of LiDAR odometry in highly dynamic scenarios, including the number , geometry distribution , and velocity of the dynamic objects , are first extensively studied using generated scenarios by leveraging real data. Instead of brutely removing the dynamic features, this paper proposes to adaptively assign weightings to the dynamic features. Then both the dynamic and static features are employed to estimate the LiDAR odometry. The effectiveness of the proposed method is verified using UrbanNav and nuScenes datasets that include numerous dynamic and static objects. To benefit the community, the implementation of the dynamic vehicle simulator and the code for the proposed method are both open-sourced.