Method for detecting long-distance barrier

Abstract

The invention provides a method for detecting a long-distance barrier and belongs to the technical field of robots. The method particularly comprises the following steps: image acquisition, image pre-processing, scene image segmentation, appearance characteristic extraction, topographic pattern judgment, topographic sample database maintenance, topographic pattern statistics modeling, statistics model parameter training and statistics model inference. The invention achieves the effective detection of a multi-mode barrier, improves the accuracy of barrier detection under the condition of unbalanced samples and improves the adaptability of the barrier detection to the changes in online real-time scenes; the topographic pattern modeling integrates the independent smooth characteristic functions and eliminates the pattern ambiguity caused by characteristic overlapping; the topographic pattern modeling integrates the correlation smooth characteristic functions and improves the online self-adaptability of the barrier detection results to the changes in real-time illumination; and the topographic pattern statistics modeling not only integrates the characteristics of the scene areas, but also theoretically integrates the spatial relationship between the scene areas and improves the stability of barrier detection under the condition of mapping deviation.

Description

technical field [0001] The invention relates to a detection method in the technical field of robots, in particular to a detection method for long-distance obstacles. Background technique [0002] Obstacle 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 obstacle detection methods, most obstacle detection methods are short-distance obstacle detection methods based on stereo vision or lidar. However, the myopic nature of this obstacle perception will directly or indirectly cause the robot to produce inefficient path planning results and even fail in navigation tasks. [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 long-distance ...

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Problems solved by technology

[0005] After searching the existing literature, it was found that Happold et al. published an article entitled "Enhanced Supervised Terrain Classification Based on Predictive Unsupervised Learning" at the Robot Science and Systems Conference in August 2006 (English name of the article: Enhancing Supervised Terrain Classification with Predictive Unsupervised Learning, conference English name: Robotics: Science and Systems II), this article discloses a long-distance obstacle detection method for robot visual navigation in unstructured environments in the wild, but this method relies too much on appearance features and mapping relationships Consistency assumption, it is still difficult to meet the following requirements of mobile robot visual navigation in outdoor unstructured environment:

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