3 accepted papers
Training networks to perform metric relocalization traditionally requires accurate image correspondences. In practice, these are obtained by restricting domain coverage, employing additional sensors, or capturing large multi-view datasets. We instead propose a self-supervised solution, which exploit
Sparse feature-based maps provide a compact representation of the environment that admit efficient algorithms, for example simultaneous localization and mapping. These representations typically assume a static world and therefore contain static map features. However, since the world contains dynamic…
We present a solution for online simultaneous localization and mapping (SLAM) self-calibration in the presence of drift in calibration parameters in order to support accurate long-term operation. Calibration parameters such as the camera focal length or camera-to-IMU extrinsics are frequently subjec…