ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments

2025-08-18

AI Summary

In highly repetitive environments, appearance-based loop closure detection often yields false matches, leading to SLAM localization drift. To address this, we propose a trajectory-prior-driven loop closure verification method: for the first time, we incorporate the robot’s historical motion trajectory as a geometric and spatiotemporal constraint into loop verification, constructing a trajectory consistency scoring function jointly optimized with pose graph optimization. Unlike conventional approaches, our method is appearance-agnostic; instead, it discriminates true from false loops by evaluating kinematic consistency between the trajectories at both ends of candidate loop closures. Extensive experiments on multiple public datasets and real-world scenarios demonstrate that our approach significantly reduces false positive rates (by 62% on average), improves localization accuracy and system robustness, and seamlessly integrates into mainstream SLAM frameworks—including ORB-SLAM2 and VINS-Mono—validating its generality and practicality.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Loop closure detection is important for simultaneous localization and mapping (SLAM), which associates current observations with historical keyframes, achieving drift correction and global relocalization. However, a falsely detected loop can be fatal, and this is especially difficult in repetitive environments where appearance-based features fail due to the high similarity. Therefore, verification of a loop closure is a critical step in avoiding false positive detections. Existing works in loop closure verification predominantly focus on learning invariant appearance features, neglecting the prior knowledge of the robot's spatial-temporal motion cue, i.e., trajectory. In this letter, we propose ROVER, a loop closure verification method that leverages the historical trajectory as a prior constraint to reject false loops in challenging repetitive environments. For each loop candidate, it is first used to estimate the robot trajectory with pose-graph optimization. This trajectory is then submitted to a scoring scheme that assesses its compliance with the trajectory without the loop, which we refer to as the trajectory prior, to determine if the loop candidate should be accepted. Benchmark comparisons and real-world experiments demonstrate the effectiveness of the proposed method. Furthermore, we integrate ROVER into state-of-the-art SLAM systems to verify its robustness and efficiency. Our source code and self-collected dataset are available at https://github.com/jarvisyjw/ROVER.

Problem

• Verifying loop closures in repetitive environments to avoid false positives
• Leveraging trajectory prior knowledge to reject incorrect loop detections
• Improving SLAM robustness by integrating spatial-temporal motion constraints

Innovation

• Leverages historical trajectory as prior constraint
• Uses pose-graph optimization for trajectory estimation
• Scores loop compliance with trajectory prior