Improved Data Association for ICP-based Scan Matching in Noisy and Dynamic Environments

This paper presents a technique to improve the data association in the iterative closest point based scan matching. The method is based on a distance-filter constructed on the basis of an analysis of the set of solutions produced by the associations in the sensor configuration space. This leads to a robust strategy to filter all the associations that do not explain the principal motion of the scan (due to noise in the sensor, large odometry errors, spurious, occlusions or dynamic features for example). The experimental results suggest that the improvement of the data association leads to more robust and faster methods in the presence of wrong correspondences.