Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion

Present object detection methods working on 3D range data are so far either optimized for unstructured offroad environments or flat urban environments. We present a fast algorithm able to deal with tremendous amounts of 3D Lidar measurements. It uses a graph-based approach to segment ground and objects from 3D lidar scans using a novel unified, generic criterion based on local convexity measures. Experiments show good results in urban environments including smoothly bended road surfaces.

[1]  José Luis Lerma,et al.  Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods , 2008 .

[2]  D. Burschka,et al.  Motion segmentation and scene classification from 3D LIDAR data , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[3]  Dirk Haehnel,et al.  Junior: The Stanford entry in the Urban Challenge , 2008 .

[4]  Roland Siegwart,et al.  Multimodal detection and tracking of pedestrians in urban environments with explicit ground plane extraction , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  G. Gate,et al.  Using targets appearance to improve pedestrian classification with a laser scanner , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[6]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[8]  Dirk Wollherr,et al.  A clustering method for efficient segmentation of 3D laser data , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  T. Rabbani,et al.  EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS , 2005 .

[10]  George Vosselman,et al.  Segmentation of point clouds using smoothness constraints , 2006 .

[11]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[12]  Roland Siegwart,et al.  Multimodal detection and tracking of pedestrians in urban environments with explicit ground plane extraction , 2008 .

[13]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Michael Himmelsbach,et al.  LIDAR-based 3D Object Perception , 2008 .

[15]  Eric L. Miller,et al.  Three-Dimensional Surface Mesh Segmentation Using Curvedness-Based Region Growing Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Ira Effenberger,et al.  Geometric Segmentation and Object Recognition in Unordered and Incomplete Point Cloud , 2003, DAGM-Symposium.

[17]  Julius Ziegler,et al.  Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge , 2008, J. Field Robotics.

[18]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  F. Nashashibi,et al.  Laser-based vehicles tracking and classification using occlusion reasoning and confidence estimation , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[20]  Christian Laugier,et al.  An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments , 2008 .

[21]  T. Rabbani,et al.  SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT , 2006 .

[22]  Sebastian Thrun,et al.  Learning Occupancy Grid Maps with Forward Sensor Models , 2003, Auton. Robots.

[23]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  André Crosnier,et al.  3D datasets segmentation based on local attribute variation , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.