Bag-of-Word-Groups (BoWG): 知覚的エイリアシング下での堅牢かつ効率的なループ閉じ込め検出法
Bag-of-Word-Groups (BoWG): A Robust and Efficient Loop Closure Detection Method Under Perceptual Aliasing
視覚単語の空間的共起を捉える「ワードグループ」を導入し、時間的一貫性を類似度計算に組み込むことで、知覚的エイリアシング環境でも高精度・高効率にループ閉じ込めを検出する手法を提案した。
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著者: Xiang Fei, Tina Tian, Howie Choset, Lu Li
分類: cs.CV, cs.RO
原文アブストラクト
Loop closure is critical in Simultaneous Localization and Mapping (SLAM) systems to reduce accumulative drift and ensure global mapping consistency. However, conventional methods struggle in perceptually aliased environments, such as narrow pipes, due to vector quantization, feature sparsity, and repetitive textures, while existing solutions often incur high computational costs. This paper presents Bag-of-Word-Groups (BoWG), a novel loop closure detection method that achieves superior precision-recall, robustness, and computational efficiency. The core innovation lies in the introduction of word groups, which captures the spatial co-occurrence and proximity of visual words to construct an online dictionary. Additionally, drawing inspiration from probabilistic transition models, we incorporate temporal consistency directly into similarity computation with an adaptive scheme, substantially improving precision-recall performance. The method is further strengthened by a feature distribution analysis module and dedicated post-verification mechanisms. To evaluate the effectiveness of our method, we conduct experiments on both public datasets and a confined-pipe dataset we constructed. Results demonstrate that BoWG surpasses state-of-the-art methods, including both traditional and learning-based approaches, in terms of precision-recall and computational efficiency. Our approach also exhibits excellent scalability, achieving an average processing time of 16 ms per image across 17,565 images in the Bicocca25b dataset.
関連論文
- 外観・意味・幾何情報を統合した堅牢で効率的な視覚ループ閉じ込め検出SLAM/ループ閉じ込め
