Graph-based Segmentation for Colored 3D Laser Point Clouds

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010

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We present an efficient graph-theoretic algorithm for segmenting a colored laser point cloud derived from a laser scanner and camera. Segmentation of raw sensor data is a crucial first step for many high level tasks such as object recognition, obstacle avoidance and terrain classification. Our method enables combination of color information from a wide field of view camera with a 3D LIDAR point cloud from an actuated planar laser scanner. We extend previous work on robust camera-only graph-based segmentation to the case where spatial features, such as surface normals, are available. Our combined method produces segmentation results superior to those derived from either cameras or laser-scanners alone. We verify our approach on both indoor and outdoor scenes.


    TITLE      = {Graph-based Segmentation for Colored {3D} Laser Point Clouds},
    AUTHOR     = {Johannes Strom and Andrew Richardson and Edwin Olson},
    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
                 Robots and Systems {(IROS)}},
    YEAR       = {2010},
    MONTH      = {October},
    VOLUME     = {},
    NUMBER     = {},
    PAGES      = { },
    KEYWORDS   = { laser point clouds, sensor fusion, graph segmentation, robot
    ISSN       = { },