Learning Long-range Terrain Perception for Autonomous Mobile Robots

Long-range terrain perception has a high value in performing efficient autonomous navigation and risky intervention tasks for field robots, such as earlier recognition of hazards, better path planning, and higher speeds. However, Stereo-based navigation systems can only perceive near-field terrain due to the nearsightedness of stereo vision. Many near-to-far learning methods, based on regions' appearance features, are proposed to predict the far-field terrain. We proposed a statistical prediction framework to enhance long-range terrain perception for autonomous mobile robots. The main difference between our solution and other existing methods is that our framework not only includes appearance features as its prediction basis, but also incorporates spatial relationships between terrain regions in a principled way. The experiment results show that our framework outperforms other existing approaches in terms of accuracy, robustness and adaptability to dynamic unstructured outdoor environments.