LAUSR

Visual place recognition (VPR) is considered among the most complicated tasks in SLAM due to the multiple challenges of drastic variations in both appearance and viewpoint. To address this issue, this article presents a self-supervised and lightweight VPR approach (namely CoCALC) that fully utilizes the appearance and geometric information provided by images. The main thing that makes CoCALC ultra-lightweight (only 0.27 MB) is our use of Depthwise Separable Convolution (DSC), a simple but effective architecture that enables our model to generate a more robust image representation. The network trained specifically for VPR can efficiently extract deep convolutional features from salient image regions that have relatively higher entropy, thereby expanding its applications on resource-limited platforms without GPUs. To further eliminate the negative consequences of the high percent false matches, a novel band-matrix-based geometric check is employed to filter out the incorrect matching of image patches, and the impact of different bandwidths on the recall rate is discussed. Results on several benchmark datasets confirm that the proposed CoCALC can yield state-of-the-art performance and superior generalization with acceptable efficiency. All relevant codes are provided at https://github.com/LiKangyuLKY/CoCALC-VPR for further studies.