A Grid-Based Proposal for Efficient Global Localisation of Mobile Robots
Man Yin Yee, J. Vermaak · 2006
In this paper we present an extension to Monte Carlo localisation (MCL) to solve the global localisation problem. This extension is in the form of an efficient data-dependent proposal that can be used both for initialisation and re-initialisation after tracking failure or robot kidnapping. The proposal is a Gaussian mixture over a fixed grid of locations, each of which has a sensor structure similar to that of the robot. The robot measurements are matched to these structures to give the best-match orientation for each grid point. The mixture components are then centred on the grid locations and best-match orientations, with the component weights proportional to the best-match likelihoods. Empirical results illustrate that our MCL approach is more computationally efficient than standard MCL, and demonstrates faster recovery from localisation failures.