Study on visual navigation system for intelligent robot in the unstructured environment

Autonomous visual navigation system is of significance for intelligent robot in the unstructured environment as it is capable to complete a series of missions i.e. capturing, processing, analyzing, understanding and decision without any priori knowledge in advance. A framework of the visual navigation system is proposed for intelligent robot under the unstructured environment in this article, which is composed of systematic composition, workflow and key algorithms such as a matching algorithm with adaptive weighted filtering computed by a hierarchical clustering approach, a local optimization algorithm for object detection based on random ferns classifier and Hough voting, an online learning algorithm for object tracking based on random ferns classifier and clustering segmentation and an algorithm for obstacle avoidance based on the calculation of maximum inscribed circle with the center of regional centroid. Finally, the validation of all the presented algorithms has been demonstrated and verified by a practical experiment for delivering the specified object through the obstacle passage by robot independently. The proposed visual system is suitable for difficult cases for example changes in object appearance, scale and pose, partial occlusions and poor illumination under the unstructured environment.