A hand

Correct cognition of the environment is the premise of mobile robots to realize autonomous navigation control tasks. The inconsistency caused by time-varying environmental information is a bottleneck for the development and application of cognitive environment technologies. In this article, we propose an environmental cognition method that uses a hand-drawn map. Firstly, we use the single skeleton refinement and fuzzy c-means algorithms to segment the image. Then, we select candidate regions combining the saliency map. At the same time, we use the superpixels straddling method to filter the windows. The final candidate object regions are obtained based on a fusion of saliency segmentation and superpixels clustering. Based on the above objectness estimation results, we use a human–computer interaction method to construct an inaccurate hand-drawn environment map for navigation. The experimental results from PASCAL VOC2007 validate the efficacy of the proposed objectness measure method, where our result of 41.2% on mean average precision is the best of the tested methods. Furthermore, the experimental results of robot navigation in the actual scene also verified the effectiveness of the proposed approach