Mobile robot navigation in unknown corridors using line and dense features of point clouds

This paper addresses the problem of mobile robot navigation in unknown corridors using RGB-Depth cameras. Instead of building a full and global 3D map of the environment, the approach exploits line and dense features extracted from RGB-D sensors. Wall-floor boundary lines are extracted from pre-processed point clouds, which ensure reliable line segmentation results compared with monocular based methods. A strategy is then proposed to compute the reference tracking points along the corridor for a wall-following behaviour. Meanwhile, dense 3D point clouds with ground-plane removed are projected which provide occupancy information, so that existing obstacle avoidance algorithms can be reused. A goal-directed navigation function is also developed by constructing a Nearness Diagram based obstacle avoidance behaviour guided by a wall-following behaviour. Experiment results validate the practicability and effectiveness of the approach.