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.
-map approaches The second category of localization techniques leverages existing maps, in order to solve the localization problem. In particular, two classes of approaches have been presented in the literature: geometry-based and projection-based methods. Caselitz al @cite_23 proposed a geometry-based method that solves the visual localization problem by comparing a set of 3D points, the point cloud reconstructed from a sequence of images and the existing map. Wolcott al @cite_30 , instead, developed a projection-based method that uses meshes built from intensity data associated to the 3D points of the maps, projected into an image plane, to perform a comparison with the camera image using the (NMI) measure. Neubert al @cite_14 proposed to use the similarity between depth images generated by synthetic views and the camera image as a score function for a particle filter, in order to localize the camera in indoor scenes.
@cite_30 @cite_14 @cite_23
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In this paper, we tackle the problem of mapping multiple 3D rigid structures and estimating their motions from perspective views through a car-mounted camera. The proposed method complements conventional localization and mapping algorithms (such as Visual Odometry and SLAM) to estimate motions of other moving objects in addition to the vehicle's motion. We present a theoretical framework for robust estimation of multiple motions and structures from perspective images. The method is based on the factorization of the projective trajectory matrix without explicit estimation of projective depth values. We exploit the epipolar geometry of calibrated cameras to generate several hypotheses for motion segments. Once the hypotheses are obtained, they are evaluated in an iterative scheme by alternating between estimation of 3D structures and estimation of multiple motions. The proposed framework does not require any knowledge about the number of motions and is robust to noisy image measurements. The method is evaluated on street-level sequences from a car-mounted camera. A benchmark dataset is also used to compare the results with previous works, although most of the related works use synthetic scenes simulating desktop environments. Current methodologies for the generation of 3D point cloud from real world scenes rely upon a set of 2D images capturing the scene from several points of view. Novel plenoptic cameras sample the light field crossing the main camera lens creating a light field image. The information available in a plenoptic image must be processed in order to render a view or create the depth map of the scene. This paper analyses a method for the reconstruction of 3D models. The reconstruction of the model is obtained from a single image shot. Exploiting the properties of plenoptic images, a point cloud is generated and compared with a point cloud of the same object but generated with a different plenoptic camera. Localizing a camera in a given map is essential for vision-based navigation. In contrast to common methods for visual localization that use maps acquired with cameras, we propose a novel approach, which tracks the pose of monocular camera with respect to a given 3D LiDAR map. We employ a visual odometry system based on local bundle adjustment to reconstruct a sparse set of 3D points from image features. These points are continuously matched against the map to track the camera pose in an online fashion. Our approach to visual localization has several advantages. Since it only relies on matching geometry, it is robust to changes in the photometric appearance of the environment. Utilizing panoramic LiDAR maps additionally provides viewpoint invariance. Yet low-cost and lightweight camera sensors are used for tracking. We present real-world experiments demonstrating that our method accurately estimates the 6-DoF camera pose over long trajectories and under varying conditions.