LAUSR

Visual place recognition (VPR) is the process of identifying previously visited places using visual information. It is crucial for a robot to achieve fast and accurate VPR under appearance and viewpoint changes. Inspired by human perception intuition, this letter proposes a lightweight semantic image descriptor called Forest for robust VPR. Specifically, for each semantic object in the image, three most recognizable reference objects are first found and ranked in descending order according to a customized measure. And then, categories, position and area of the semantic object itself, along with the categories of the three recognized reference objects, are used to encode the local descriptor of the object. As a result, the set of local descriptors is used as the global descriptor of the image. Performance of the proposed descriptor is evaluated and compared with six state-of-the-art VPR methods, Ibow-LCD, CoHOG, NetVLAD, LOST-X, Region VLAD, Patch-NetVLAD, over four public datasets of urban scenes, Extended-CMU Season, RobotCar Seasons v2, SYNTHIA and Kitti, using three Precision-Recall based metrics, AUC(Area Under Curve), Recall@100%Precision and Precision@100%Recall. The results show that our method maintains competitive accuracy in scenes with appearance and viewpoint changes and exhibits high robustness. At the same time, our method is robust to the noise of semantic segmentation and exhibits high real-time performance.