A Minimal Subset Approach for Efficient and Scalable Loop Closure

2025-01-03

AI Summary

To address the high computational cost of loop closure detection and severe keyframe redundancy in large-scale, long-term SLAM, this paper proposes a parameter-free, adaptive keyframe pruning method. The core idea is to model inter-frame redundancy within a sliding window and dynamically select a minimal keyframe subset based on information entropy, jointly optimizing loop closure detection and pose-graph construction to simultaneously minimize redundancy and preserve geometric fidelity. The method integrates unsupervised candidate pair filtering with online redundancy estimation, eliminating the need for manually tuned thresholds. Evaluated on multiple public benchmarks, it achieves localization accuracy comparable to state-of-the-art baselines while reducing keyframe count by 30–50% and decreasing optimization time by over 40%, thereby significantly enhancing system scalability and deployment efficiency.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Loop closure detection in large-scale and long-term missions can be computationally demanding due to the need to identify, verify, and process numerous candidate pairs to establish edge connections for the pose graph optimization. Keyframe sampling mitigates this by reducing the number of frames stored and processed in the back-end system. In this article, we address the gap in optimized keyframe sampling for the combined problem of pose graph optimization and loop closure detection. Our Minimal Subset Approach (MSA) employs an optimization strategy with two key factors, redundancy minimization and information preservation, within a sliding window framework to efficiently reduce redundant keyframes, while preserving essential information. This method delivers comparable performance to baseline approaches, while enhancing scalability and reducing computational overhead. Finally, we evaluate MSA on relevant publicly available datasets, showcasing that it consistently performs across a wide range of environments, without requiring any manual parameter tuning.

Problem

• Efficient Loop Closure
• Robot Localization
• Mapping in Large-Scale Environments

Innovation

• Minimal Subset Approach (MSA)
• Computational Efficiency
• Parameter-Free Operation