Region Based SLAM-Aware Exploration: Efficient and Robust Autonomous Mapping Strategy That Can Scale

2025-04-14

AI Summary

This paper addresses autonomous exploration and mapping in large-scale, unknown indoor environments. We propose a SLAM-aware region-partitioned exploration framework. Its core contributions are: (1) a novel region-level SLAM stabilization mechanism enabling block-wise incremental exploration and stable submap fusion; (2) lightweight mapping via keyframe marginalization and sparse pose-graph optimization; and (3) an integrated robust relocalization and checkpoint-based fault-tolerant recovery system for resilient resume-on-interruption. Experiments in real-world home and office environments demonstrate that our method reduces the number of keyframes by 85%, submap usage by 32–50%, and pose-graph optimization time by 78–80%, while shortening total exploration time by 10–15%. These improvements significantly enhance mapping efficiency, robustness, and scalability, outperforming current state-of-the-art approaches.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Autonomous exploration for mapping unknown large scale environments is a fundamental challenge in robotics, with efficiency in time, stability against map corruption and computational resources being crucial. This paper presents a novel approach to indoor exploration that addresses these key issues in existing methods. We introduce a Simultaneous Localization and Mapping (SLAM)-aware region-based exploration strategy that partitions the environment into discrete regions, allowing the robot to incrementally explore and stabilize each region before moving to the next one. This approach significantly reduces redundant exploration and improves overall efficiency. As the device finishes exploring a region and stabilizes it, we also perform SLAM keyframe marginalization, a technique which reduces problem complexity by eliminating variables, while preserving their essential information. To improves robustness and further enhance efficiency, we develop a check- point system that enables the robot to resume exploration from the last stable region in case of failures, eliminating the need for complete re-exploration. Our method, tested in real homes, office and simulations, outperforms state-of-the-art approaches. The improvements demonstrate substantial enhancements in various real world environments, with significant reductions in keyframe usage (85%), submap usage (50% office, 32% home), pose graph optimization time (78-80%), and exploration duration (10-15%). This region-based strategy with keyframe marginalization offers an efficient solution for autonomous robotic mapping.

Problem

• Efficient autonomous mapping of large unknown environments
• Robust SLAM-aware region-based exploration strategy
• Reducing redundant exploration and computational resources

Innovation

• SLAM-aware region-based exploration strategy
• Keyframe marginalization reduces complexity
• Checkpoint system for failure recovery