Graph-based SLAM approach for environments with laser scan ambiguity

The study of UGVs (Unmanned Ground Vehicles) has been actively promoted and researched. In order to operate unmanned vehicles or robots autonomously, it is necessary to understand the surrounding environment and know where it is. This paper proposes a novel localization method using a monocular camera and a laser scanner for a robot in an environment that is difficult to localize. We exploited the hybrid method between depth data from a laser scanner and the image feature of the camera, and then the collected robot pose information is estimated using the pose graph structure. In order to verify the performance of the proposed algorithm, experiments are conducted in an indoor environment. The results of the proposed algorithm are then numerically compared with the results of the conventional method. We confirmed that it is possible to localize the robot in environments with laser scan ambiguity such as a long corridor.