Conference Paper

3D Environment Reconstruction Using Modified Color ICP Algorithm by Fusion of a Camera and a 3D Laser Range Finder

Robot Res. Dept., Electron. & Telecommun. Res. Inst., Daejeon, South Korea
DOI: 10.1109/IROS.2009.5354500 Conference: Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on
Source: IEEE Xplore


In this paper, we propose a system which reconstructs the environment with both color and 3D information. We perform extrinsic calibration of a camera and a LRF (laser range finder) to fuse 3D information and color information of objects. We also formularize an equation to measure the result of the calibration. Moreover, we acquire 3D data by rotating 2D LRF with camera, and use ICP (iterative closest point) algorithm to combine data acquired in other places. We use the SIFT (scale invariant feature transform) matching for the initial estimation of ICP algorithm. It offers accurate and stable initial estimation robust to motion change compare to odometry. We also modify the ICP algorithm using color information. Computation time of ICP algorithm can be reduced by using color information.

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    • "ICP quantizes I and Q to classify reference points. The corresponding cell of a new point on the IQ plane is decided by I and Q of the new point, and it searches for the closest point in the cell [4]. Color-supported generalized ICP is a color-supported variant of the generalized ICP (gICP), which is a variant of ICP [5]. "

    Preview · Article · Feb 2016
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    • "The hue (from the Hue-saturation-lightness model) of each point is classified and used as a filter to constrain the closest point search in every ICP iteration [17] [18]. Color data on range image can be used to estimate initial alignment of pair wise scans via Scale Invariant Feature Transform (SIFT), color attributes transferred in YIQ color model are weighted to construct new variant together with range information for ICP fine registration [16]. Depth-interpolated Image Feature (DIFT) algorithm solves corresponding points between 2 imagery and register color point cloud based on extracted correspondences [19]. "
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    ABSTRACT: This paper presents methodologies to accelerate the registration of 3D point cloud segments by using hue data from the associated imagery. The proposed variant of the Iterative Closest Point (ICP) algorithm combines both normalized point range data and weighted hue value calculated from RGB data of an image registered 3D point cloud. A k-d tree based nearest neighbor search is used to associated common points in {x, y, z, hue} 4D space. The unknown rigid translation and rotation matrix required for registration is iteratively solved using Singular Value Decomposition (SVD) method. A mobile robot mounted scanner was used to generate color point cloud segments over a large area. The 4D ICP registration has been compared with typical 3D ICP and numerical results on the generated map segments shows that the 4D method resolves ambiguity in registration and converges faster than the 3D ICP.
    Full-text · Conference Paper · Jun 2011
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    ABSTRACT: The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640×480 px), our implementation achieved ICP runtimes of < 20 ms on an off-the-shelf consumer GPU.
    Full-text · Conference Paper · Jan 2011
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