[1]WU Ming,LI Linlin,WEI Zhenhua,et al.A robot multi-object tracking algorithm in unknown environments[J].CAAI Transactions on Intelligent Systems,2015,10(3):448-453.[doi:10.3969/j.issn.1673-4785.201405051]
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A robot multi-object tracking algorithm in unknown environments

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 448-453 Column: 学术论文—智能系统 Public date: 2015-06-25
Title:
A robot multi-object tracking algorithm in unknown environments
Author(s):
WU Ming;  LI Linlin;  WEI Zhenhua;  WANG Hongqiao
Command Information Engineering Department, The Second Artillery Engineering College, Xian 710025, China
Keywords:
robot; simultaneous localization and mapping (SLAM); multi-object tracking; particle filtering; joint integrated probabilistic data association (JIPDA); Rao-Blackwellized particle filtering; Kalman filtering
CLC:
TP242.6
DOI:
10.3969/j.issn.1673-4785.201405051
Abstract:
In this paper, a particle filtering algorithm based on the joint integrated probabilistic data association (JIPDA) is proposed in order to solve the problem of motile robot multi-object tracking in unknown environments. The Rao-Blackwellized particle filtering is reconstructed based on the JIPDA in the new algorithm. It allows the robot to estimate joint states of itself, environment features and multi-object states simultaneously. The algorithm divides the system variables into two parts: the lineal variable representing multi-object and environment feature states, and the non-linear variable representing robot states. The system state is updated by JIPDA Kalman filtering and particle filtering. Estimation precision of robot states, environment feature states and multi-object states is verified by simulation results, verifying the ability of multi-object tracking in unknown environments.
References:
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