SR-SLAM: Scene-reliability Based RGB-D SLAM in Diverse Environments

2025-09-01

AI Summary

To address poor adaptability and insufficient environmental awareness in SLAM caused by unstable feature quantity and quality in dynamic environments, this paper proposes a scene-reliability–driven RGB-D SLAM framework. Methodologically, it introduces a unified scene reliability assessment mechanism that fuses multi-source metrics and historical observations to enable environment-aware adaptive behavior control. The framework integrates adaptive dynamic region selection, depth-assisted feature clustering, reliability-weighted pose refinement, and keyframe optimization—synergistically combining direct and feature-based approaches. Evaluated on public benchmarks and real-world scenarios, the method achieves up to a 90% improvement in localization accuracy and robustness over state-of-the-art dynamic SLAM methods, significantly enhancing perceptual reliability under complex, time-varying conditions.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Visual simultaneous localization and mapping (SLAM) plays a critical role in autonomous robotic systems, especially where accurate and reliable measurements are essential for navigation and sensing. In feature-based SLAM, the quantityand quality of extracted features significantly influence system performance. Due to the variations in feature quantity and quality across diverse environments, current approaches face two major challenges: (1) limited adaptability in dynamic feature culling and pose estimation, and (2) insufficient environmental awareness in assessment and optimization strategies. To address these issues, we propose SRR-SLAM, a scene-reliability based framework that enhances feature-based SLAM through environment-aware processing. Our method introduces a unified scene reliability assessment mechanism that incorporates multiple metrics and historical observations to guide system behavior. Based on this assessment, we develop: (i) adaptive dynamic region selection with flexible geometric constraints, (ii) depth-assisted self-adjusting clustering for efficient dynamic feature removal in high-dimensional settings, and (iii) reliability-aware pose refinement that dynamically integrates direct methods when features are insufficient. Furthermore, we propose (iv) reliability-based keyframe selection and a weighted optimization scheme to reduce computational overhead while improving estimation accuracy. Extensive experiments on public datasets and real world scenarios show that SRR-SLAM outperforms state-of-the-art dynamic SLAM methods, achieving up to 90% improvement in accuracy and robustness across diverse environments. These improvements directly contribute to enhanced measurement precision and reliability in autonomous robotic sensing systems.

Problem

• Enhancing adaptability in dynamic feature culling and pose estimation
• Improving environmental awareness in SLAM assessment and optimization
• Addressing feature quantity and quality variations across diverse environments

Innovation

• Scene reliability assessment with multiple metrics
• Adaptive dynamic region selection with geometric constraints
• Reliability-aware pose refinement integrating direct methods