Toward Robust LiDAR based 3D Object Detection via Density-Aware Adaptive Thresholding
<|reference_start|>Toward Robust LiDAR based 3D Object Detection via Density-Aware Adaptive Thresholding: Robust 3D object detection is a core challenge for autonomous mobile systems in field robotics. To tackle this issue, many researchers have demonstrated improvements in 3D object detection performance in datasets. However, real-world urban scenarios with unstructured and dynamic situations can still lead to numerous false positives, posing a challenge for robust 3D object detection models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. 3D object detection models usually perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. While conventional perception algorithms typically employ a single threshold in post-processing, the proposed algorithm addresses this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in urban scenarios. The results show performance enhancements in 3D object detection models across a range of scenarios, not only in dynamic urban road conditions but also in scenarios involving adverse weather conditions.<|reference_end|>
@article{lee2024toward,
    title={Toward Robust LiDAR based 3D Object Detection via Density-Aware Adaptive
  Thresholding},
    author={Eunho Lee, Minwoo Jung, Ayoung Kim},
    journal={arXiv preprint arXiv:2404.13852},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13852},
    primaryClass={cs.RO}
}