This work describes a method for calibration of the Velodyne HDL-64E scanning LIDAR system. The principal contribution was expressed by a pattern calibration signature, the mathematical model and the numerical algorithm for computing the calibration parameters of the LIDAR. In this calibration pattern the main objective is to minimize systematic errors due to geometric calibration factor. It describes an algorithm for solution of the intrinsic and extrinsic parameters. Finally, its uncertainty was calculated from the standard deviation of calibration result errors.
In this document, we study the problem of optimally placing a mixture of directional and omnidirectional cameras. In our solution, the workspace is represented by an occupancy grid map [1]. Then, using surface-projected workspace and camera perception models, we develop a binary integer programming algorithm. The results of the algorithm are applied successfully to a variety of simulated scenarios.
In this paper, we present an approach to qualitative rover localization with panoramic images. The approach relies on the possibility to efficiently and robustly compute the resemblance between panoramic images, indexing them by histograms of local appearances. A database of image indexes is dynamically built during rover motions: when the rover re-perceives an already crossed area, it matches the current image with the stored ones (place recognition), and thus gets a qualitative estimate of its position. Experimental results on a 400 images database illustrates the algorithms throughout the paper.
This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems.
Mobile platforms typically combine several data acquisition systems such as lasers, cameras and inertial systems. However the geometrical combination of the different sensors requires their calibration, at least, through the definition of the extrinsic parameters, i.e., the transformation matrices that register all sensors in the same coordinate system. Our system generate an accurate association between platform sensors and the estimated parameters including rotation, translation, focal length, world and sensors reference frame. The extrinsic camera parameters are computed by Zhang's method using a pattern composed of white rhombus and rhombus holes, and the LIDAR with the results of previous work. Points acquired by the LIDAR are projected into images acquired by the Ladybug cameras. A new calibration pattern, visible to both sensors is used. Correspondence is obtained between each laser point and its position in the image, the texture and color of each point of LIDAR can be know.
This work presents the implementation of a 3D reconstruction system capable of reconstructing a 360-degree scene with a single acquisition using a projection of patterns. The system is formed by two modules: the first module is a CCD camera with a parabolic mirror that allows the acquisition of catadioptric images. The second module consists of a light projector and a parabolic mirror that is used to generate the pattern projections over the object that will be reconstructed. The projection system has a 360-degree field of view and both modules were calibrated to obtain the extrinsic parameters. To validate the functionality of the system, we performed 3D reconstructions of three objects, and show the reconstruction error analysis.
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