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