关键词: 移动机器人, 行人跟踪, 粒子滤波, 深度信息, 社交力, 二级粒子

Abstract: People tracking for mobile robot is an important application which reflects the intelligence of robot, and it has wide prospect and practical value. However, there exists a big challenge because of the complexity of environment and the uncertainty of people moving. This paper improves the particle filter for mobile robot people tracking based on the analysis of particle filter framework. On the hand, the color information, depth information and social force are combined to estimate the similarity between the template and candidate, which increases the tracking precision. On the other hand, a concept of secondary particle is proposed to overcome the loss of particle??s diversity, which increases the tracking accuracy. In the last, the improved particle filter, sequential importance resampling (SIR) particle filter and extended Kalman filter (EKF) algorithm are compared on the turtlebot robot and the public data IAS-Lab. Results prove the superiority of improved particle filter.

Key words: mobile robot, people tracking, particle filter, depth information, social force, secondary particle