About this Journal Submit a Manuscript Table of Contents
Journal of Robotics
Volume 2011 (2011), Article ID 683975, 10 pages
http://dx.doi.org/10.1155/2011/683975
Research Article

People Detection Based on Spatial Mapping of Friendliness and Floor Boundary Points for a Mobile Navigation Robot

1Toshiba Corporate Research & Development Center, 1, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, Japan
2Toshiba Corporation, Power Systems Company, 1-1, Shibaura 1-Chome, Minato-ku, Tokyo 105-8001, Japan
3Department of Engineering Science and Mechanics, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto-ku, Tokyo 135-8548, Japan
4Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan

Received 14 July 2011; Revised 7 November 2011; Accepted 8 November 2011

Academic Editor: Danica Kragic

Copyright © 2011 Tsuyoshi Tasaki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Navigation robots must single out partners requiring navigation and move in the cluttered environment where people walk around. Developing such robots requires two different people detections: detecting partners and detecting all moving people around the robots. For detecting partners, we design divided spaces based on the spatial relationships and sensing ranges. Mapping the friendliness of each divided space based on the stimulus from the multiple sensors to detect people calling robots positively, robots detect partners on the highest friendliness space. For detecting moving people, we regard objects’ floor boundary points in an omnidirectional image as obstacles. We classify obstacles as moving people by comparing movement of each point with robot movement using odometry data, dynamically changing thresholds to detect. Our robot detected 95.0% of partners while it stands by and interacts with people and detected 85.0% of moving people while robot moves, which was four times higher than previous methods did.