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Title: SLAM with MTT : theory and initial results [mobile robot localisation]
Authors: Huang, GQ
Rad, AB
Wong, YK
Ip, YL
Keywords: Kalman filters
Monte Carlo methods
Filtering theory
Mobile robots
Path planning
Target tracking
Issue Date: 2004
Publisher: IEEE
Source: 2004 IEEE Conference on Robotics, Automation and Mechatronics, 1-3 December 2004, v. 2, p. 834-839 How to cite?
Abstract: To make a robot to work for and with human, the ability to simultaneously localize itself, accurately map its surroundings, and safely detect and track moving objects around it is a key prerequisite for a truly autonomous robot. In this paper, we explore the theoretical framework of this problem, i.e. simultaneous localization and mapping (SLAM) with multiple target tracking (MTT), and propose to employ sequential Monte Carlo methods (SMCM) as robust and computationally efficient algorithm. After mathematically formulating the problem, we apply a Rao-Blackwellized particle filter to solve SLAM which is partitioned into robot pose and feature location estimations and a conditioned particle filter to solve MTT which is partitioned into robot pose and moving object state estimations, both filters conditioned on robot pose. In detail, we propose sampling importance resampling (SIR) method to estimate robot pose, extended Kalman filter (EKF) to estimate feature location, and hybrid independent/coupled sample-based joint probability data association filter (Hyb-SJPDAF) to solve tracking and data association problem. We present some preliminary experimental results to demonstrate the capabilities of our approach.
ISBN: 0-7803-8645-0
DOI: 10.1109/RAMECH.2004.1438026
Appears in Collections:Conference Paper

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