Dynamic Object Removal of Static 3D Point-Cloud Map Building in Casualty Collection Point

Understanding the environment is crucial for the autonomous navigation of vehicles. Accurately identifying and removing dynamic objects that cause occlusions and noisy pose issues is crucial to the task. The casualty collection point (CCP) is a designated location for treating casualties during disasters. These sites are commonly located in open fields to ensure that the injured receive timely and appropriate care. Therefore, the construction of a 3D map in CCP may encounter additional challenges. In this paper, we introduces a novel algorithm that integrates a learning-based multi-object detection network with a Kalman Filter tracking framework to remove dynamic object traces from the 3D map building, particularly in CCP scenarios, and it redirects attention on the velocity attribute of dynamic objects at the object-level scale. Additionally, we contribute a new dataset specifically designed for the CCP scenario. Comparative experiments conducted on the SemanticKITTI dataset and CCP dataset show that our proposed method achieves the state-of-the-art performance in removing traces of dynamic objects on 3D Map. Dataset is available at https://github.com/haidongwang96/ccp_dataset.