Vision-based pole-like obstacle detection and localization for urban mobile robots

Despite the enormous progress of the last years, urban environments still represent a challenge for robot autonomous navigation. This paper focuses on the problem of detecting street pole-like obstacles using a monocular camera. Such obstacles, due to their thin structure, may be difficult to be detected by common active sensors like lasers. This is even more critical for innovative solid state LiDARs like the one employed in this work because, at the actual state, they are characterized by very low angular resolutions. The approach described here is based on identifying poles as long vertical structures in the image and in locating them with respect to the robot using a Kalman filter based depth estimation. This information can then be fused with the information coming from LiDARs realizing a complete obstacle detection module.