TOSS: Real-time Tracking and Moving Object Segmentation for Static Scene Mapping
Seoyeon Jang, Minho Oh, Byeongho Yu, I Made Aswin Nahrendra, Seungjae, Lee, Hyungtae Lim, and Hyun Myung

TL;DR
This paper introduces TOSS, an integrated real-time framework for autonomous robot navigation that combines moving object segmentation and static map building, enabling safe operation in complex environments.
Contribution
It presents a novel real-time framework that unifies online tracking-based moving object segmentation with static map construction for improved autonomous navigation.
Findings
Effective dynamic object segmentation in real-time
Accurate static map building with dynamic object removal
Successful evaluation in challenging environments
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization
