[1]WU Ming,SUN Jiyin.Simultaneous localization, mapping and object tracking in an unknown environment using particle filtering[J].CAAI Transactions on Intelligent Systems,2013,8(2):168-176.[doi:10.3969/j.issn.1673-4785.201202001]
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Simultaneous localization, mapping and object tracking in an unknown environment using particle filtering

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 2 Page number: 168-176 Column: 学术论文—智能系统 Public date: 2013-04-25
Title:
Simultaneous localization, mapping and object tracking in an unknown environment using particle filtering
Author(s):
WU Ming;  SUN Jiyin
The department of commander information system， The PLA Second Artillery Engineering College, Xi’an 710025, China
Keywords:
Rao-Blackwellized particle filter;  simultaneous localization and mapping;  object tracking
CLC:
TP242.6
DOI:
10.3969/j.issn.1673-4785.201202001
Abstract:
The proposed research paper examines a simultaneous localization, mapping, and object tracking method. The examination was in part based on a particle filter that allows a robot to track an object in an unknown environment. This method utilizies the Rao-Blackwellized particle filting to estimate the pose of robot, landmarks distribution, and object position simultaneously. The general distribution of a particle swarm represents the pose of a robot, and each particle includes two kinds of Extended Kalman Filter (EKF). One EKF estimates distribution of landmarks, while the other EKF estimates the state of the object. The weight of particle is determined by the combination of two likelihoods, one is the likelihood between particle state and landmarks, and the other is the likelihood between particle state and object state. The results of the research indicate the valid robot experimentation and simulation, confirm the proposed research approach is very effective.
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