High accuracy localization is a basic requirement for autonomous vehicles navigation. However, in urban environments, Global Navigation Satellite Systems (GNSS) suffer from Non-Line of Sight (NLoS) signals, multipath and sometimes a limited number of visible satellites, degrading the localization accuracy. Maps with georeferenced features are a means to address this issue. In this paper, an open access map with cadastral footprints of the buildings is used for localization. Buildings are stable over time and provide visible features in cities. Using 2D footprints of the buildings provides little detailed information, but when they are matched with long range omnidirectional LiDARs, a good quality estimated pose can be achieved. We present a method that uses the Normal Distributions Transform (NDT) to match several layers of a LiDAR scan with the map. A fast filtering method based on local linear regression is proposed to extract aligned points in the LiDAR scans which filters out the largest part of the outliers before applying the NDT optimization. The performance of the approach is evaluated on real data recorded with an experimental vehicle equipped with a ground truth. The results show that this approach is able to provide high accuracy consistent with autonomous navigation tasks.

This paper presents a new decentralized approach for collaborative localization and map update relying on landmarks measurements performed by the robots themselves. The method uses a modified version of the Kalman filter, namely Schmidt Kalman filter that approaches the performance of the optimal centralized Kalman filter without the need to update each robot pose. To deal with data incest and limited communication, the computation of cross-covariance errors between robots must be well managed. Each robot individually updates its own map, the map fusion is performed by using the unweighted Kullback-Leibler Average to keep estimation consistency. The performance of the approach is evaluated in a simulation environment where robots are equipped with odometry and a lidar for exteroceptive perception. The results show that collaboration improves the localization of the robots and the estimation of the map while maintaining consistency.