A mixed model data association for simultaneous localisation and mapping in dynamic environments

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.