Human Following of Mobile Robot With a Low-cost Laser Scanner

Abstract

Human-following robots can bring a lot of convenience to our lives. The function of human-following can be decomposed into three processes: human detection, tracking and following. Considering cost and adaptability. In this paper, a low-cost laser scanner is used to achieve it. And an improved method of threshold-based clustering is proposed, which can effectively solve the problem of excessive clustering when it is far away from the laser scanner. Three types of features are extracted to recognize the legs from other objects by the random forest classifier. The position in the middle of the legs is used to indicate the position of the target person, which will be tracked by the robot using a Kalman filter algorithm and nearest neighbor algorithm. After inputting the coordinates of the followed target into the controller, the linear velocity and angular velocity of the following can be obtained. Experiments show that the robot can follow the target person stably.