Title: A mixed model data association for simultaneous localisation and mapping in dynamic environments
Authors: Rex H. Wong; Jizhong Xiao; Samleo L. Joseph; Shouling He
Addresses: Department of Engineering and Technology, Vaughn College of Aeronautics and Technology, New York, NY 11369, USA ' Department of Electrical Engineering, The City College of the City University of New York, New York, NY 10031, USA ' Department of Electrical Engineering, The City College of the City University of New York, New York, NY 10031, USA ' Department of Engineering and Technology, Vaughn College of Aeronautics and Technology, New York, NY 11369, USA
Abstract: This paper presents a feasible and robust approach which handles the real-time data association problem for robotic simultaneous localisation and mapping (SLAM) in clutter and dynamic environment. Unlike most of proposals that are based on a single model to estimate the dynamics of a scenario and thus fall short of the variation of environmental complexity which may need different models to estimate the modes of behaviour, in this paper, we propose an integrated schema which mixes the interactive multiple model (IMM) and joint probabilistic data association (JPDA), with the asymmetric assignment optimisation algorithm to generate the optimal feasible hypothesis. Because of its adaptability and cost-effectiveness, this approach can be applied for real-time SLAM applications. Simulation is performed to demonstrate the effectiveness of this method.
Keywords: interactive multiple models; IMM; joint probabilistic data association; JPDA; simultaneous localisation and mapping; SLAM; clutter; asymmetric assignment optimisation; robot localisation; robot mapping; mobile robots; indoor environments; object avoidance; robot navigation; simulation; dynamic environments; modelling.
International Journal of Mechatronics and Automation, 2013 Vol.3 No.1, pp.1 - 15
Available online: 14 Mar 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article