text: In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained on the working area, i.e. CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.26m and 1.05deg median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17deg. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.
  @cite_10: In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing approaches, RegNet casts all three conventional calibration steps (feature extraction, feature matching and global regression) into a single real-time capable CNN. Our method does not require any human interaction and bridges the gap between classical offline and target-less online calibration approaches as it provides both a stable initial estimation as well as a continuous online correction of the extrinsic parameters. During training we randomly decalibrate our system in order to train RegNet to infer the correspondence between projected depth measurements and RGB image and finally regress the extrinsic calibration. Additionally, with an iterative execution of multiple CNNs, that are trained on different magnitudes of decalibration, our approach compares favorably to state-of-the-art methods in terms of a mean calibration error of 0.28° for the rotational and 6 cm for the translation components even for large decalibrations up to 1.5 m and 20°.
summary: The work presented in this paper has been inspired by Schneider al @cite_10 , which used 3D scans from a LIDAR and RGB images as the input of a novel CNN, RegNet. Their goal was to provide a CNN-based method for calibrating the extrinsic parameters of a camera a LIDAR sensor. Taking inspiration from that work, in this paper we propose a novel approach that has the advantages of both the categories described above. Differently from the aforementioned literature contribution, which exploits the data gathered from a synchronized single activation of a 3D LIDAR and a camera image, the inputs of our approach are a complete 3D LIDAR map of the environment, together with a single image and a rough initial guess of the camera pose. Eventually, the output consists of an accurate 6-DoF camera pose localization. It is worth to notice that having a single LIDAR scan taken at the same time as the image imply that the observed scene is exactly the same. In our case, instead, the 3D map usually depicts a different configuration, road users are not present, making the matching more challenging.