An LSTM Network for RealTime Odometry Estimation The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, lightweight and lowcost. However, since only a 2D slice of the surrounding environment is detected at each scan, it is a challenge to execute important tasks such as the localization of the vehicle. In this paper we present a novel framework that explores the use of deep Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using only 2D laser scanners. The application of RCNNs provides the tools to not only extract the features of the laser scanner data using Convolutional Neural Networks (CNNs), but in addition it models the possible connections among consecutive scans using the Long ShortTerm Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in realtime without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.

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.