Robust 3D object detection remains a pivotal concern in the domain of autonomous field robotics. Despite notable enhancements in detection accuracy across standard datasets, real-world urban environments, characterized by their unstructured and dynamic nature, frequently precipitate an elevated incidence of false positives, thereby undermining the reliability of existing detection paradigms. In this context, our study introduces an advanced post-processing algorithm that modulates detection thresholds dynamically relative to the distance from the ego object. Traditional perception systems typically utilize a uniform threshold, which often leads to decreased efficacy in detecting distant objects. In contrast, our proposed methodology employs a Neural Network with a self-adaptive thresholding mechanism that significantly attenuates false negatives while concurrently diminishing false positives, particularly in complex urban settings. Empirical results substantiate that our algorithm not only augments the performance of 3D object detection models in diverse urban and adverse weather scenarios but also establishes a new benchmark for adaptive thresholding techniques in field robotics.

翻译：鲁棒的3D目标检测仍是自主野外机器人领域的核心议题。尽管在标准数据集上的检测精度已取得显著提升，但真实城市环境因其非结构化与动态特性，常导致假阳性率升高，削弱了现有检测范式的可靠性。针对这一背景，本研究提出一种先进的后处理算法，可根据自车目标距离动态调节检测阈值。传统感知系统通常采用固定阈值，这往往导致远距离目标检测效能下降。相比之下，本方法采用具备自适应阈值机制的神经网络，在复杂城市场景中显著减少假阴性目标的同时有效抑制误检。实验结果表明，该算法不仅增强了3D目标检测模型在多样化城市及恶劣天气条件下的性能，更为野外机器人领域的自适应阈值技术树立了新标杆。