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Graph-Based Segmentation for Colored 3D Laser Point Clouds

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Executive Summary

The authors 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. Their 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. They extend previous work on robust camera-only graph-based segmentation to the case where spatial features, such as surface normals, are available. Their combined method produces segmentation results superior to those derived from either cameras or laser-scanners alone.

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