学習ベース局所記述子による幾何学的マルチセッション地図統合
Geometric Multi-Session Map Merging with Learned Local Descriptors
異なるセッションで取得した大規模点群地図を、キーポイント対応エンコーダと平面幾何トランスフォーマで抽出した特徴量により位置合わせし、ループ閉じ込みと相対姿勢推定を行って統合する手法を提案した論文。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yanlong Ma, Nakul S. Joshi, Christa S. Robison, Philip R. Osteen, Brett T. Lopez
分類: cs.RO, cs.CV
原文アブストラクト
Multi-session map merging is crucial for extended autonomous operations in large-scale environments. In this paper, we present GMLD, a learning-based local descriptor framework for large-scale multi-session point cloud map merging that systematically aligns maps collected across different sessions with overlapping regions. The proposed framework employs a keypoint-aware encoder and a plane-based geometric transformer to extract discriminative features for loop closure detection and relative pose estimation. To further improve global consistency, we include inter-session scan matching cost factors in the factor-graph optimization stage. We evaluate our framework on the public datasets, as well as self-collected data from diverse environments. The results show accurate and robust map merging with low error, and the learned features deliver strong performance in both loop closure detection and relative pose estimation.
