FastSLAM 2.0: Least-Squares Approach

In this paper, we present a set of robust and efficient algorithms with O(N) cost for the following situations: object detection with a laser ranger; mobile robot pose estimation and a FastSLAM improved implementation. Objected detection is mainly based on a novel multiple line fitting method, related with walls at the environment. This method assumes that walls at the environment constitute a regular constrained angles. A line-based pose estimation method is also proposed, based on least-squares (LS). This method performs the matching of detected lines and estimated map lines and it can provide the global pose estimation under assumption of known data-association. FastSLAM 1.0 has been improved by considering the estimated pose with the LS-approach to re-allocate each particle of the posterior distribution. This approach has a lower computational cost than EKF approach in FastSLAM 2.0. The three algorithms have been combined in order to perform an efficient self-localization and map building process, tested for indoor environments with real data. And results show that the ideas proposed in this paper could aim in closing the loop and also to improve the overall estimation performance