Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking.

ABSTRACT: Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple

SUBMITTER: Sualeh M 

PROVIDER: S-EPMC6470994 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature