Senekal, FP2010-08-162010-08-162009-11Senekal, FP. 2009. Fast and robust road segmentation and obstacle map generation for autonomous navigation. 3rd Robotics and Mechatronics Symposium (ROBMECH 2009). 8-10 November 2009, CSIR International Convention Centre, Pretoria9780620447218http://hdl.handle.net/10204/41493rd Robotics and Mechatronics Symposium (ROBMECH 2009). 8-10 November 2009, CSIR International Convention Centre, PretoriaThe ability to detect and navigate drivable road surfaces is an important research area in autonomous navigation for use in autonomous vehicles. In this paper, a probabilistic computer vision algorithm for segmentation of tarred road surfaces is developed. Using a calibrated camera, a projection of a local obstacle map is then laid over the segmented image and an estimate is made of the likelihood of drivable region in each occupancy cell. The algorithm is both fast, can be implemented in real-time systems and robust, road surfaces are segmented well. The method was tested on a set of test images captured from a camera mounted on an autonomous vehicle. Good classification results are achieved, making it possible to use the algorithm and the resulting obstacle map in conjunction with global and local path planning algorithms to achieve autonomous navigation.enRoad segmentationComputer vision algorithmCalibrated cameraAlgorithmsAutonomous navigationMechatronicsRoboticsROBMECH 2009Conference PresentationSenekal, F. (2009). Fast and robust road segmentation and obstacle map generation for autonomous navigation. http://hdl.handle.net/10204/4149Senekal, FP. "Fast and robust road segmentation and obstacle map generation for autonomous navigation." (2009): http://hdl.handle.net/10204/4149Senekal F, Fast and robust road segmentation and obstacle map generation for autonomous navigation; 2009. http://hdl.handle.net/10204/4149 .TY - Conference Presentation AU - Senekal, FP AB - The ability to detect and navigate drivable road surfaces is an important research area in autonomous navigation for use in autonomous vehicles. In this paper, a probabilistic computer vision algorithm for segmentation of tarred road surfaces is developed. Using a calibrated camera, a projection of a local obstacle map is then laid over the segmented image and an estimate is made of the likelihood of drivable region in each occupancy cell. The algorithm is both fast, can be implemented in real-time systems and robust, road surfaces are segmented well. The method was tested on a set of test images captured from a camera mounted on an autonomous vehicle. Good classification results are achieved, making it possible to use the algorithm and the resulting obstacle map in conjunction with global and local path planning algorithms to achieve autonomous navigation. DA - 2009-11 DB - ResearchSpace DP - CSIR KW - Road segmentation KW - Computer vision algorithm KW - Calibrated camera KW - Algorithms KW - Autonomous navigation KW - Mechatronics KW - Robotics KW - ROBMECH 2009 LK - https://researchspace.csir.co.za PY - 2009 SM - 9780620447218 T1 - Fast and robust road segmentation and obstacle map generation for autonomous navigation TI - Fast and robust road segmentation and obstacle map generation for autonomous navigation UR - http://hdl.handle.net/10204/4149 ER -