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.

翻译：鲁棒的三维目标检测是自主移动系统在野外机器人领域面临的核心挑战。为解决这一问题，许多研究者展示了在数据集中三维目标检测性能的提升。然而，现实城市环境中存在的非结构化动态场景仍会导致大量误检，对鲁棒的三维目标检测模型构成挑战。本文提出一种后处理算法，该算法根据自车距离动态调整目标检测阈值。三维目标检测模型通常在近距离目标检测中表现良好，但对远距离目标的检测性能可能欠佳。传统感知算法通常在后处理中采用单一阈值，而本文提出的算法通过采用基于自车距离的自适应阈值来解决此问题，从而在城市场景中最小化漏检并减少误检。结果表明，该算法在多种场景下均能提升三维目标检测模型的性能，不仅适用于动态城市道路环境，还可应用于恶劣天气条件。