SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map

• URL: http://arxiv.org/abs/2311.02831v3
• Date: Thu, 9 Nov 2023 12:55:34 GMT
• Title: SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map
• Authors: Zhenzhong Cao
• Abstract summary: Loop closure is one of the crucial components in SLAM. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data.
• Score: 0.8158530638728501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Loop closure, as one of the crucial components in SLAM, plays an essential role in correcting the accumulated errors. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data, making them less versatile and robust in real-world scenarios, leading to missed detections or false positives detections in loop closure. To address these issues, we first propose a object-level data association method based on multi-level verification, which can associate 2D semantic features of current frame with 3D objects landmarks of map. Next, taking advantage of these association relations, we introduce a semantic loop closure method based on quadric-level object map topology, which represents scenes through the topological graph of objects and achieves accurate loop closure at a wide field of view by comparing differences in the topological graphs. Finally, we integrate these two methods into a complete object-aware SLAM system. Qualitative experiments and ablation studies demonstrate the effectiveness and robustness of the proposed object-level data association algorithm. Quantitative experiments show that our semantic loop closure method outperforms existing state-of-the-art methods in terms of precision, recall and localization accuracy metrics.

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