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.
TOSS: Real-Time Tracking and Moving Object Segmentation for Static Scene Mapping
Lect. Notes in Networks, Syst.
International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023
2024-11-22
13 pages
Article/Chapter (Book)
Electronic Resource
English
FACHARTIKEL Rekonstruktion Überdeckung Töss Verkehrsprovisorien
Online Contents | 1999
|A real time moving-scene holographic camera
NTRS | 1973
|TOSS -- Tiros operational satellite system
Engineering Index Backfile | 1963
|Sparse Scene Flow Segmentation for Moving Object Detection in Urban Environments
British Library Conference Proceedings | 2011
|