  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.
