TOSS: Real-time Tracking and Moving Object Segmentation for Static Scene Mapping

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 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.