Online Visual Place Recognition via Saliency Re-identification
- URL: http://arxiv.org/abs/2007.14549v1
- Date: Wed, 29 Jul 2020 01:53:45 GMT
- Title: Online Visual Place Recognition via Saliency Re-identification
- Authors: Han Wang, Chen Wang and Lihua Xie
- Abstract summary: Existing methods often formulate visual place recognition as feature matching.
Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks, we formulate visual place recognition as saliency re-identification.
In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise.
- Score: 26.209412893744094
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As an essential component of visual simultaneous localization and mapping
(SLAM), place recognition is crucial for robot navigation and autonomous
driving. Existing methods often formulate visual place recognition as feature
matching, which is computationally expensive for many robotic applications with
limited computing power, e.g., autonomous driving and cleaning robot. Inspired
by the fact that human beings always recognize a place by remembering salient
regions or landmarks that are more attractive or interesting than others, we
formulate visual place recognition as saliency re-identification. In the
meanwhile, we propose to perform both saliency detection and re-identification
in frequency domain, in which all operations become element-wise. The
experiments show that our proposed method achieves competitive accuracy and
much higher speed than the state-of-the-art feature-based methods. The proposed
method is open-sourced and available at
https://github.com/wh200720041/SRLCD.git.
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