Real-time obstacle detection using stereo vision for autonomous ground vehicles: A survey

One of the most important features for any intelligent ground vehicle is based on how is reliable and complete the perception of the environment and the capability to discriminate what an obstacle is. Obstacle Detection (OD) is one of the most widely discussed topics in literature. Many approaches have been presented for different application fields and scenarios; in last years most of them have been revisited using stereo vision or 2D/3D sensor technologies. In this paper we present a brief survey about Obstacle Detection techniques based on stereo vision for intelligent ground vehicles, describing and comparing the most interesting approaches. In order to provide a generic overview of these techniques, it has been decided to focus the study only on the algorithms that have provided a major contribution through real-time experiments in unsupervised scenarios.

[1]  Allen R. Hanson,et al.  Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Daniel Cremers,et al.  Detection and Segmentation of Independently Moving Objects from Dense Scene Flow , 2009, EMMCVPR.

[3]  S. Thrun.  Learning Occupancy Grids With Forward Sensor Models , 2002 .

[4]  Hans P. Moravec.  Robot spatial perception by stereoscopic vision and 3D evidence grids , 1996 .

[5]  A. Bensrhair,et al.  Sensors for Obstacle Detection - A Survey , 2007, 2007 30th International Spring Seminar on Electronics Technology (ISSE).

[6]  Alberto Broggi,et al.  Terrain mapping for off-road Autonomous Ground Vehicles using rational B-Spline surfaces and stereo vision , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

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

[8]  Uwe Franke,et al.  The Stixel World - A Compact Medium Level Representation of the 3D-World , 2009, DAGM-Symposium.

[9]  Wolfram Burgard,et al.  OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems , 2010 .

[10]  C. Laugier,et al.  Real-time moving obstacle detection using optical flow models , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[11]  Alberto Broggi,et al.  A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[12]  Paolo Zani,et al.  Incremental disparity space image computation for automotive applications , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Andres Huertas,et al.  Stereo-vision-based terrain mapping for off-road autonomous navigation , 2009, Defense + Commercial Sensing.

[14]  Julius Ziegler,et al.  Sparse scene flow segmentation for moving object detection in urban environments , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[15]  Sergiu Nedevschi,et al.  A Particle-Based Solution for Modeling and Tracking Dynamic Digital Elevation Maps , 2014, IEEE Transactions on Intelligent Transportation Systems.

[16]  Uwe Franke,et al.  6D-Vision: Fusion of Stereo and Motion for Robust Environment Perception , 2005, DAGM-Symposium.

[17]  Luca Gatti,et al.  VIAC: An out of ordinary experiment , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[18]  Uwe Franke,et al.  Towards a Global Optimal Multi-Layer Stixel Representation of Dense 3D Data , 2011, BMVC.

[19]  Alberto Broggi,et al.  Stereo obstacle detection in challenging environments: The VIAC experience , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  W. Kruger,et al.  Real-time estimation and tracking of optical flow vectors for obstacle detection , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[21]  Julius Ziegler,et al.  StereoScan: Dense 3d reconstruction in real-time , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[22]  Takeo Kanade,et al.  High-Resolution Terrain Map from Multiple Sensor Data , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[24]  C. Rabe,et al.  Fast detection of moving objects in complex scenarios , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[25]  Rudolf Mester,et al.  Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming , 2008 .

[26]  Roberto Manduchi,et al.  Fast and reliable obstacle detection and segmentation for cross-country navigation , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

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

[28]  Alberto Broggi,et al.  PROUD-Public road urban driverless test: Architecture and results , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.