Online Visual Place Recognition via Saliency Re-identification
This addresses the problem of efficient place recognition for robots with limited computing power, such as in autonomous driving, though it is an incremental improvement over existing methods.
The paper tackles the computational expense of visual place recognition in robotics by reformulating it as saliency re-identification, achieving competitive accuracy and significantly higher speed compared to state-of-the-art feature-based methods.
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.