Sucar E. and Hayet J. B.,”Bayesian scale estimation for monocular slam based on generic object detection for correcting scale drift”. IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21-25, 2018.
We propose a novel real-time algorithm for estimating the local scale correction of a monocular SLAM system, to obtain a correctly scaled version of the 3D map and of the camera trajectory. Within a Bayesian framework, it integrates observations from a deep-learning based generic object detector and landmarks from the map whose projection lie inside a detection region, to produce scale correction estimates from single frames. For each observation, a prior distribution on the height of the detected object class is used to define the observation's likelihood. Due to the scale drift inherent to monocular SLAM systems, we also incorporate a rough model on the dynamics of scale drift. Quantitative evaluations are presented on the KITTI dataset, and compared with different approaches. The results show a superior performance of our proposal in terms of relative translational error when compared to other monocular systems based on object detection.