Scene Representations for Autonomous Driving: An Approach Based on Polygonal Primitives

In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.

[1]  A. Bykat,et al.  Convex Hull of a Finite Set of Points in Two Dimensions , 1978, Inf. Process. Lett..

[2]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[3]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[4]  Gabriel Taubin,et al.  The ball-pivoting algorithm for surface reconstruction , 1999, IEEE Transactions on Visualization and Computer Graphics.

[5]  Michael M. Kazhdan,et al.  Poisson surface reconstruction , 2006, SGP '06.

[6]  K. Dietmayer,et al.  Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[7]  Adrião Duarte Dória Neto,et al.  An Adaptive Learning Approach for 3-D Surface Reconstruction From Point Clouds , 2008, IEEE Trans. Neural Networks.

[8]  Zoltan-Csaba Marton,et al.  On fast surface reconstruction methods for large and noisy point clouds , 2009, 2009 IEEE International Conference on Robotics and Automation.

[9]  Andreas Birk,et al.  3-D perception and modeling , 2009, IEEE Robotics & Automation Magazine.

[10]  Zoltan-Csaba Marton,et al.  On Fast Surface Reconstruction Methods for Large and Noisy Datasets , 2009, IEEE International Conference on Robotics and Automation.

[11]  Mark E. Campbell,et al.  Probabilistic estimation of Multi-Level terrain maps , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Wolfram Burgard,et al.  Editorial: Three-dimensional mapping, part 1 , 2009 .

[13]  Luke Fletcher,et al.  A High-rate, Heterogeneous Data Set From The DARPA Urban Challenge , 2010, Int. J. Robotics Res..

[14]  Sergiu Nedevschi,et al.  Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection , 2010, IEEE Transactions on Vehicular Technology.

[15]  R. Jovanovic,et al.  Compression of volumetric data using 3D Delaunay triangulation , 2011, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization.

[16]  Shang-Hong Lai,et al.  An Orientation Inference Framework for Surface Reconstruction From Unorganized Point Clouds , 2011, IEEE Transactions on Image Processing.

[17]  Kun Zhou,et al.  Data-Parallel Octrees for Surface Reconstruction. , 2011, IEEE transactions on visualization and computer graphics.

[18]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.