Fusing Lidar Data and Aerial Imagery with Perspective Correction for Precise Localization in Urban Canyons This paper addresses a vehicle localization method that fuses aerial maps and lidar data in urban canyon environments where global positioning system (GPS) signals are inaccurate. The boundaries of buildings are extracted from the aerial map and they are matched to point cloud data provided by the lidar. However, most aerial maps contain perspective projection distortions which can be significant in urban canyons with tall buildings. In this study, a new method to correct such projection distortion is proposed and it is applied to precise localization by fusing the corrected map and lidar data. In order to achieve this, the semantic segmentation of an aerial image is performed using a convolutional neural network, and the mutual information between the lidar measurements and the building boundaries is obtained to measure their similarity. A particle filter framework is employed to localize the vehicle and match the map using the mutual information as the weight of a particle. An experimental dataset is then used to validate the feasibility of the proposed method.

Deep LearningBased Mutual Detection and Collaborative Localization for Mobile Robot Fleets Using Solely 2D LIDAR Sensors Localization for mobile robots in dynamic, largescale environments is a challenging task, especially when relying solely on odometry and 2D LIDAR data. When operating in fleets, mutual detection and the exchange of localization information can be highly valuable. Detecting and classifying different robot types in a heterogeneous fleet, however, is nontrivial with 2D LIDAR data due to the sparse observation information.In this paper a novel approach for mutual robot detection, classification and relative pose estimation based on a combination of convolutional and ConvLSTM layers is presented in order to solve this issue. The algorithm learns an endtoend classification and pose estimation of robot shapes using 2D LIDAR information transformed into a gridmap. Subsequently a mixture model representing the probability distribution of the pose measurement for each robot type is extracted out of the heatmap output of the network. The output is then used in a cloudbased collaborative localization system in order to increase the localization of the individual robots.The effectiveness of our approach is demonstrated in both, simulation and realworld experiments. The results of our evaluation show that the classification network is able to achieve a precision of 90% on realworld data with an average position estimation error of 14 cm. Moreover, the collaborative localization system is able to increase the localization accuracy of a robot equipped with lowcost sensors by 63%.