Global detection method of unstructured outdoor terrains

Abstract

The invention provides a global detection method of unstructured outdoor terrains in the technical field of robots. The method comprises the specific steps of image acquisition, image preprocessing, scene image segmentation, appearance characteristic extraction, terrain type judgment, terrain classifier candidate database establishment, current scene optimal classifier verification, terrain type statistical modeling, statistical model parameter training and statistical model reasoning. According to the global detection method, the navigation experience of different time points is integrated in principle, and the stability and the intelligence degree of terrain detection under the condition of uneven and even deficient view field samples are improved; a combined multi-layer sensor classifier adopted for the global detection method takes the uncertainty and the nonlinearity of the appearance-terrain mapping relation of the scenes into consideration, so that the accuracy of terrain detection can be improved; in addition, the space relations at variable scales in the current scene and the compatibility of types are modeled in principle, and the global detection of the unstructured outdoor terrains is realized.

Description

technical field [0001] The invention relates to a detection method in the technical field of robots, in particular to a global detection method of unstructured outdoor terrain. Background technique [0002] Terrain detection is a key problem to be solved in the visual navigation of mobile robots in outdoor unstructured environments, and it is a prerequisite for subsequent path planning and action execution. At present, due to the lack of low-cost and long-distance terrain detection methods, most terrain detection methods are short-distance detection methods based on stereo vision or lidar. However, the myopic nature of this terrain detection will directly or indirectly cause the robot to produce inefficient path planning results or even fail the navigation task. [0003] In recent years, with the development of artificial intelligence and other disciplines and the self-learning ability of robots has received greater attention in the field of robotics, the near-to-far terrai...

AI Technical Summary

Problems solved by technology

However, this method relies too much on the aforementioned consistency assumption of the mapping relationship, and it is difficult to meet the following requirements for visual navigation of mobile robots in outdoor unstructured environments:

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