Abstract:

For the environment detection and obstacle avoidance problems of mobile robots, the information acquired by the sensors was not comprehensive and accurate, and it was impossible to accurately provide information about the surrounding environment. In this paper, the use of Kinect sensors to obtain the color information and depth data of the surrounding environment was proposed. A depth data map obtained by the Kinect sensor using gradient partitioning and DBSCAN clustering method. Firstly, the gradient obstacle edge detection method was used to quickly and efficiently process the edge contour of the obstacle obtained by Kinect, and the difference parameters in the algorithm are improved, so that the calculated gradient result was more accurate. Then compare the different clustering methods, use BDSCAN clustering method to cluster the detected obstacles, and finally verify the algorithm by arranging specific experiments. The experimental results showed that the algorithm can accurately divide the surrounding obstacles, and the feasible area was effective. The successful detection rate for different objects was more accurate. The validity of the algorithm was verified.

Key words: Kinect, cluster analysis, BDSCAN, gradient division, obstacle contour detection