ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments

• URL: http://arxiv.org/abs/2009.13454v1
• Date: Mon, 28 Sep 2020 16:31:29 GMT
• Title: ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments
• Authors: Mihnea-Alexandru Tomit\u{a}, Mubariz Zaffar, Michael Milford, Klaus McDonald-Maier and Shoaib Ehsan
• Abstract summary: Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place under changing viewpoints and appearances. We present a new handcrafted VPR technique that achieves state-of-the-art place matching performance under challenging conditions.
• Score: 19.437998213418446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place under changing viewpoints and appearances. A large number of handcrafted and deep-learning-based VPR techniques exist, where the former suffer from appearance changes and the latter have significant computational needs. In this paper, we present a new handcrafted VPR technique that achieves state-of-the-art place matching performance under challenging conditions. Our technique combines the best of 2 existing trainingless VPR techniques, SeqSLAM and CoHOG, which are each robust to conditional and viewpoint changes, respectively. This blend, namely ConvSequential-SLAM, utilises sequential information and block-normalisation to handle appearance changes, while using regional-convolutional matching to achieve viewpoint-invariance. We analyse content-overlap in-between query frames to find a minimum sequence length, while also re-using the image entropy information for environment-based sequence length tuning. State-of-the-art performance is reported in contrast to 8 contemporary VPR techniques on 4 public datasets. Qualitative insights and an ablation study on sequence length are also provided.

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