Viewpoint-Tolerant Place Recognition Combining 2D and 3D Information for UAV Navigation

The booming interest in Unmanned Aerial Vehicles (UAV s) is fed by their potentially great impact, however progress is hindered by their limited perception capabilities. While vision-based odometry was shown to run successfully onboard UAV s, loop-closure detection to correct for drift or to recover from tracking failures, has so far, proven particularly challenging for UAVs. At the heart of this is the problem of viewpoint-tolerant place recognition; in stark difference to ground robots, UAVs can revisit a scene from very different viewpoints. As a result, existing approaches struggle greatly as the task at hand violates underlying assumptions in assessing scene similarity. In this paper, we propose a place recognition framework, which exploits both efficient binary features and noisy estimates of the local 3D geometry, which are anyway computed for visual-inertial odometry onboard the UAV. Attaching both an appearance and a geometry signature to each `location', the proposed approach demonstrates unprecedented recall for perfect precision as well as high quality loop-closing transformations on both flying and hand-held datasets exhibiting large viewpoint and appearance changes as well as perceptual aliasing.