Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 7, Pages 7896-7909Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3074520
Keywords
Loop-closure detection; SLAM; feature matching; place recognition; ASMK
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Funding
- National Natural Science Foundation of China [62003247, 61773295, 61903279]
- Key Research and Development Program of Hubei Province [2020BAB113]
- Natural Science Fund of Hubei Province [2019CFA037]
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In this paper, a novel appearance-based loop-closure detection (LCD) method is proposed, with a focus on real-time geometrical verification using the local relative orientation matching (LRO) algorithm. The method significantly improves the LCD performance and outperforms current state-of-the-art methods on six publicly available datasets.
Loop-closure detection (LCD), which aims to recognize a previously visited location, is a crucial component of the simultaneous localization and mapping system. In this paper, a novel appearance-based LCD method is presented. In particular, we propose a simple yet surprisingly useful feature matching algorithm for real-time geometrical verification of candidate loop-closures, termed as local relative orientation matching (LRO). It aims to efficiently establish reliable feature correspondences based on preserving local topological structures between the query image and candidate frame. To effectively retrieve candidate loop closures, we introduce the aggregated selective match kernel framework into the LCD task, which can effectively represent images and reduce the quantization noise of the traditional bag-of-words framework. In addition, the SuperPoint neural network is employed to extract reliable interest points and feature descriptors. Extensive experimental results demonstrate that our LRO can significantly improve the LCD performance, and the proposed overall LCD method can achieve much better performance over the current state-of-theart on six publicly available datasets.
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