Vision-based mobile robot's SLAM and navigation in crowded environments

Vision-based mobile robot's simultaneous localization and mapping (SLAM) and navigation has been the source of countless research contributions because of rich sensory output and cost effectiveness of vision sensors. However, existing methods of vision-based SLAM and navigation are not effective for robots to be used in crowded environments such as train stations and shopping malls, because when we extract feature points from an image in crowded environments, many feature points are extracted from not only static objects but also dynamic objects such as humans. By recognizing all such feature points as landmarks, the algorithm collapses and errors occur in map building and self-localization. In this paper, we propose a SLAM and navigation method that is effective even in crowded environments by extracting robust 3D feature points from sequential vision images and odometry. By using the proposed method, we can eliminate unstable feature points extracted from dynamic objects and perform SLAM and navigation stably. We present experiments showing the utility of our approach in crowded environments, including map building and navigation.