Particle Filter based Scan Correlation

The process of scancorrelation is very useful in map building and localization in environments without any easily discernible landmarks. It involves estimating the relative transform between two given scans of data. This can be formulated as a posterior estimation problem over the transformation space. Since the posterior, the action model and the likelihood function are mostly nonlinear, a particle filter is used to estimate this posterior [1]. A GMM based correlation scheme has been used in [2] to drive the scan correlation process. In this paper, we propose a 3 step particle filtering approach that combines the simplicity and efficiency of the particle filter with the robustness of the GMM based correlation scheme. Experimental results are provided to verify the effectiveness of this approach.