Autonomous navigation in harsh and dynamic 3D environments poses a great challenge for modern Robotics. This work presents a novel traversability analysis and path-planning technique that processes 3D pointcloud maps to generate terrain gradient information. An analysis of terrain roughness and presence of obstacles is applied on the perceived environment in order to generate efficient paths. These avoid major hills when more conservative paths are available, potentially promoting fuel economy and reducing the wear of the equipment and the associated risks. The proposed approach outperforms existing techniques based on results in realistic 3D simulation scenarios, which are discussed in detail.
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