Abstract
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.
Highlights
Navigation in an unknown and unstructured outdoor environment is a fundamental and challenging problem for autonomous mobile robots
4.1.3 Qualitative Results The qualitative results for different data sets are shown in Fig. 4, Fig. 6 and Fig. 7, in which left columns show original RGB images, middle columns are related to the classification results of modified k-nearest neighboring (MKNN), and right columns concern the classification results of CRFNFP
The high performance of CRFNFP and MKNN for ground category is due to the fact that many ground pixels are located in the perception range of stereo vision and can be recognized
Summary
Navigation in an unknown and unstructured outdoor environment is a fundamental and challenging problem for autonomous mobile robots. To address nearsighted navigational errors, near-to-farlearning-based, long-range perception approaches are developed, which collect both appearances and stereo information from the near field as inputs for training appearance-based models and applies these models in the far field in order to predict safe terrain and obstacles farther out from the robot where stereo readings are unavailable Appearance-based methods assume that the near-field mapping from the appearance to traversability is the same as the far-field mapping Such an assumption does not necessarily hold due to the complex terrain geometry and varying lighting conditions in unstructured outdoor environment. All the existing near-to-far approaches rely excessively on appearance features and the mapping assumption As a result, they lack the robustness and selfadaptability for changling illuminant conditions. The tops of the hay bales which receive near-field stereo labels of “obstacle”- are
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