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Place recognition is a core component of Simultaneous Localization andMapping (SLAM) algorithms. Particularly in visual SLAM systems,previously-visited places are recognized by measuring the appearance similaritybetween images representing these locations. However, such approaches aresensitive to visual appearance change and also can be computationallyexpensive. In this paper, we propose an alternative approach adapting LiDARdescriptors for 3D points obtained from stereo-visual odometry for placerecognition. 3D points are potentially more reliable than 2D visual cues (e.g.,2D features) against environmental changes (e.g., variable illumination) andthis may benefit visual SLAM systems in long-term deployment scenarios.Stereo-visual odometry generates 3D points with an absolute scale, whichenables us to use LiDAR descriptors for place recognition with highcomputational efficiency. Through extensive evaluations on standard benchmarkdatasets, we demonstrate the accuracy, efficiency, and robustness of using 3Dpoints for place recognition over 2D methods.

A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors