Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation.
@article{arxiv.1801.02686,
title = {Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties},
author = {Achim Kampker and Mohsen Sefati and Arya Abdul Rachman and Kai Kreisköther and Pascual Campoy},
journal= {arXiv preprint arXiv:1801.02686},
year = {2018}
}
Comments
Some significant editorial/editing issues are found upon review. Paper will undergo language re-proofing before resubmitted