The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, light-weight and low-cost. 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 Short-Term Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in real-time without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.
@cite_3: Although 2D LiDAR based Simultaneous Localization and Mapping (SLAM) is a relatively mature topic nowadays, the loop closure problem remains challenging due to the lack of distinctive features in 2D LiDAR range scans. Existing research can be roughly divided into correlation based approaches e.g. scan-to-submap matching and feature based methods e.g. bag-of-words (BoW). In this paper, we solve loop closure detection and relative pose transformation using 2D LiDAR within an end-to-end Deep Learning framework. The algorithm is verified with simulation data and on an Unmanned Aerial Vehicle (UAV) flying in indoor environment. The loop detection ConvNet alone achieves an accuracy of 98.2 in loop closure detection. With a verification step using the scan matching ConvNet, the false positive rate drops to around 0.001 . The proposed approach processes 6000 pairs of raw LiDAR scans per second on a Nvidia GTX1080 GPU.
In our work we chose to use as the sensor only a 2D laser scanner, which could reduce considerably the price of future intelligent vehicles. At this moment, @cite_3 were the only authors to use this kind of sensor to estimate odometry by the use of a deep learning approach. They propose a network to perform scan matching and loop closure using CNNs. Although the loop closure networks shows good accuracy, the scan matching results are still very inaccurate compared to classic methods.