2024/08/10 by Seoyeon Jang, Minho Oh, Jang, Seoyeon +11 · 1 citation
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2408.05453
openalex publication_date 2024/08/10 · openalex created_date 2024/09/11 · openalex updated_date 2026/07/28
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.