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%.

Deep Sensor Fusion for RealTime Odometry Estimation Cameras and 2D laser scanners, in combination, are able to provide lowcost, lightweight and accurate solutions, which make their fusion wellsuited for many robot navigation tasks. However, correct data fusion depends on precise calibration of the rigid body transform between the sensors. In this paper we present the first framework that makes use of Convolutional Neural Networks (CNNs) for odometry estimation fusing 2D laser scanners and monocameras. The use of CNNs provides the tools to not only extract the features from the two sensors, but also to fuse and match them without needing a calibration between the sensors. We transform the odometry estimation into an ordinal classification problem in order to find accurate rotation and translation values between consecutive frames. Results on a real road dataset show that the fusion network runs in realtime and is able to improve the odometry estimation of a single sensor alone by learning how to fuse two different types of data information.