ICRA 2011 Paper Abstract

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Paper TuA1-InteracInterac.13

Chou, Chen Tun (National Taiwan University), Li, Jiun-Yi (National Taiwan University), Fu, Li-Chen (National Taiwan University), Chang, Ming-Fang (National Taiwan University)

Multi-Robot Cooperation Based Human Tracking System Using Laser Range Finder

Scheduled for presentation during the Poster Sessions "Interactive Session I: Robotic Technology" (TuA1-InteracInterac), Tuesday, May 10, 2011, 08:20−09:35, Hall

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 21, 2019

Keywords Human detection & tracking

Abstract

In this paper, we develop a multi-human detection system with a team of robots basically in an indoor environment. To start with, we propose a hybrid approach to resolve the problem of human leg detection using Laser Range Finder (LRF) for each robot, that returns not only true-or-false type of answer but also a probability. Specifically, the set of measurement data obtained from the LRF mounted on a robot is further decomposed into several sectors using an appropriate segmentation technique. Then, we apply a probabilistic model to compare these sectors with leg patterns to check if any of them belongs to the set of human leg patterns or not.

For the entire multi-human detection system, each robot of the team delivers the detected human information to our central control computer through the Inter-Process Communication (IPC). With prior map information of the residing environment and supposing each robot in the team has a localization module, we can then map these results of human detection from every robot into their global coordinates after process of data association. But in order to reduce the computational complexity while doing the data association among these robots in a team, we introduce a set of appropriate rules. Finally, we apply a particle filter based tracking algorithm to keep accurate track of people being detected and to improve the robustness of the detection outcome.

 

 

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