SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and
  Opposing Viewpoints
Spencer Carmichael Rahul Agrawal Ram Vasudevan Katherine A. Skinner
Recognizing places from an opposing viewpoint during a return trip is a
common experience for human drivers. However, the analogous robotics
capability, visual place recognition (VPR) with limited field of view cameras
under 180 degree rotations, has proven to be challenging to achieve. To address
this problem, this paper presents Same Place Opposing Trajectory (SPOT), a
technique for opposing viewpoint VPR that relies exclusively on structure
estimated through stereo visual odometry (VO). The method extends recent
advances in lidar descriptors and utilizes a novel double (similar and
opposing) distance matrix sequence matching method. We evaluate SPOT on a
publicly available dataset with 6.7-7.6 km routes driven in similar and
opposing directions under various lighting conditions. The proposed algorithm
demonstrates remarkable improvement over the state-of-the-art, achieving up to
91.7% recall at 100% precision in opposing viewpoint cases, while requiring
less storage than all baselines tested and running faster than all but one.
Moreover, the proposed method assumes no a priori knowledge of whether the
viewpoint is similar or opposing, and also demonstrates competitive performance
in similar viewpoint cases.