Coevolution Based Adaptive Monte Carlo Localization

Abstract

Self-localization, a basic problem in mobile robot systems, can be divided into two subproblems: pose tracking and global localization. In pose tracking, the initial robot pose is known, and localization seeks to identify small, incremental errors in a robot’s odometry (Leonard & Durrant-Whyte, 1991). In global localization, however the robot is required to estimate its pose by local and incomplete observed information under the condition of uncertain initial pose. Global localization is a more challenging problem. Only most recently, several approaches based on probabilistic theory are proposed for global localization, including grid-based approaches (Burgard et al., 1996), topological approaches (Kaelbling et al., 1996) (Simmons & Koenig, 1995), Monte Carlo localization (Dellaert et al., 1999) and multi-hypothesis tracking (Jensfelt & Kristensen, 2001) (Roumeliotis & Bekey, 2000). By representing probability densities with sets of samples and using the sequential Monte Carlo importance sampling (Andrieu & Doucet, 2002), Monte Carlo localization (MCL) can represent non-linear and non-Gaussian models well and focus the computational resources on regions with high likelihood. So MCL has attracted wide attention and has been applied in many real robot systems. But traditional MCL has some shortcomings. Since samples are actually drawn from a proposal density, if the observation density moves into one of the tails of the proposal density, most of the samples’ non-normalized importance factors will be small. In this case, a large sample size is needed to represent the true posterior density to ensure stable and precise localization. Another problem is that samples often too quickly converge to a single, high likelihood pose. This might be undesirable in the case of localization in symmetric environments, where multiple distinct hypotheses have to be tracked for extended periods of time. How to get higher localization precision, to improve efficiency and to prevent premature convergence of MCL are the key concerns of the researchers. To make the samples represent the posterior density better, Thrun et al. proposed mixtureMCL (Thrun et al., 2001), but it needs much additional computation in the sampling process. To improve the efficiency of MCL, methods adjusting sample size adaptively over time are proposed (Fox, 2003) (Koller & Fratkina, 1998), but they increase the probability of premature convergence. Although clustered particle filters are applied to solve premature convergence (Milstein et al., 2002), the method loses the advantage of focusing the computational resources on regions with high likelihood because it maintains the same sample size for all clusters. In this paper, a new version of MCL is proposed to overcome those limitations. Samples are clustered into groups which are also called species. A coevolutionary model derived from competition of ecological species is introduced to