領域ベースSLAM対応探索：スケーラブルで効率的かつ堅牢な自律マッピング戦略
Region Based SLAM-Aware Exploration: Efficient and Robust Autonomous Mapping Strategy That Can Scale
環境を領域に分割して逐次探索・安定化し、SLAMのキーフレーム削減とチェックポイント復帰を組み合わせることで、大規模未知環境の自律マッピングを効率化・堅牢化した研究。
著者: Megha Maheshwari, Sadeigh Rabiee, He Yin, Martin Labrie, Hang Liu, Rajasimman Madhivanan
分類: cs.RO
原文アブストラクト
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 checkpoint 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.
