Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition

• URL: http://arxiv.org/abs/2202.06470v1
• Date: Mon, 14 Feb 2022 03:20:39 GMT
• Title: Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition
• Authors: Y. Shen, R. Wang, W. Zuo, N. Zheng
• Abstract summary: We propose a tightly coupled learning (TCL) strategy to train triplet models. It combines global and local descriptors for joint optimization. Our lightweight unified model is better than several state-of-the-art methods.
• Score: 0.09558392439655011
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchical architecture, which consists of retrieving top-N candidates using global features, and re-ranking top-N with local features. However, since the two types of features are usually processed independently, re-ranking may harm global retrieval, termed re-ranking confusion. Moreover, re-ranking is limited by global retrieval. In this paper, we propose a tightly coupled learning (TCL) strategy to train triplet models. Different from original triplet learning (OTL) strategy, it combines global and local descriptors for joint optimization. In addition, a bidirectional search dynamic time warping (BS-DTW) algorithm is also proposed to mine locally spatial information tailored to VPR in re-ranking. The experimental results on public benchmarks show that the models using TCL outperform the models using OTL, and TCL can be used as a general strategy to improve performance for weakly supervised ranking tasks. Further, our lightweight unified model is better than several state-of-the-art methods and has over an order of magnitude of computational efficiency to meet the real-time requirements of robots.

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