This work presents a method to improve vehicle localization by using the information from a prior occupancy grid to bound the possible poses. The method, named Map-Aware Particle Filter, uses a nonlinear approach to map-matching that can be integrated into a particle filter framework for localization. Each particle is re-weighted based on the validity of its current position in the map. In addition, we buffer the trajectory followed by the vehicle and then append it to each particle's pose. We then quantify the overlap between the trajectory and the map's free space. This serves as a measure of each particle's validity given the trajectory and the shape of the map. We evaluated the method by performing experiments with different types of localization sensors: First, (i) we significantly reduced the drift inherent to dead reckoning. By only using wheel odometry and map information we achieved loop closure over a distance of approximately 3 km. We also (ii) increased the accuracy of GPS localization. Finally, (iii) we fused a fragile 2D LiDAR localization with the map information. The resulting system had a higher robustness and managed to close the loop in an outdated map where it had failed before.

The ability to drive autonomously in heterogeneous environments without GPS, pattern identification (e.g. road following), or artificial landmarks is key to field robotics. To address this challenge, we present a complete waypoint navigation framework for unmanned ground vehicles. A Velodyne PUCK VLP-16 LiDAR and an IMU are mounted on an autonomous, full size utility vehicle and used for localization within a previously created base map. We redesign a six degrees of freedom LiDAR SLAM algorithm to achieve 3D localization on the base map, as well as real-time vehicle navigation. We fuse the low-frequency, high precision SLAM updates with high-frequency, odometric local state estimates from the vehicle. The navigation costmap consists of a 2D occupancy grid which is computed from the 3D base map. Relying on this setup, the vehicle is capable of navigating through a complex site completely autonomously. The test site has densely and sparsely built areas, bushland, industrial activities, pedestrians, and other manned or unmanned vehicles. Extensive testing was done using both current and outdated base maps for comparisons, and a high precision RTK-GPS was used for ground truth. So far, more than 60 km of completely autonomous driving has been performed without a single system or navigation failure.