TOSS: Real-time Tracking and Moving Object Segmentation for Static Scene Mapping
<|reference_start|>TOSS: Real-time Tracking and Moving Object Segmentation for Static Scene Mapping: Safe navigation with simultaneous localization and mapping (SLAM) for autonomous robots is crucial in challenging environments. To achieve this goal, detecting moving objects in the surroundings and building a static map are essential. However, existing moving object segmentation methods have been developed separately for each field, making it challenging to perform real-time navigation and precise static map building simultaneously. In this paper, we propose an integrated real-time framework that combines online tracking-based moving object segmentation with static map building. For safe navigation, we introduce a computationally efficient hierarchical association cost matrix to enable real-time moving object segmentation. In the context of precise static mapping, we present a voting-based method, DS-Voting, designed to achieve accurate dynamic object removal and static object recovery by emphasizing their spatio-temporal differences. We evaluate our proposed method quantitatively and qualitatively in the SemanticKITTI dataset and real-world challenging environments. The results demonstrate that dynamic objects can be clearly distinguished and incorporated into static map construction, even in stairs, steep hills, and dense vegetation.<|reference_end|>
@article{jang2024toss:,
    title={TOSS: Real-time Tracking and Moving Object Segmentation for Static Scene
  Mapping},
    author={Seoyeon Jang, Minho Oh, Byeongho Yu, I Made Aswin Nahrendra, Seungjae
  Lee, Hyungtae Lim, and Hyun Myung},
    journal={arXiv preprint arXiv:2408.05453},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05453},
    primaryClass={cs.RO}
}