An Effective Ground Plane Extraction using Depth Map Estimation from a Kinect Device
Main Article Content
Abstract
This paper presents a new approach to extract ground planes from a depth map which is provided by Kinect. The proposed system applies an robust algorithm to calculate the depth gradient maps (GDM) with high accuracy. Then the correct partition provides a set of candidates for the selection of ground. Last, it uses an efficient filter to find out the truth ground planes. The results prove the certainty of the algorithm in both cases consisting of the perfect data and actual scenes. For first case, the percentage of truth ground pixel detection R1 is common over 90%. The percentage of incorrect ground pixels detection R2 is lower than 5%. For the second case, the process of implementing the proposed algorithm on a depth map from Kinect also is compared with RANSAC algorithm and Enhanced V-Disparity algorithm. The result demonstrates that the proposed method's R1 is usually greater than RANSAC method and V-Disparity method 2%, while R2 of the proposed method is less than half of R2 of the compared methods, respectively. The experimental results show the ability to respond in real time when this work is deployed as a stereo vision-based navigation system.
Keywords
Depth map, gradient, ground plane, Kinect, vehicle
Article Details
References
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[17] Wikimedia Foundation, Inc. https://en.wikipedia.org/wiki/Kinect.
[18] Middlebury College, Microsoft Research, and the National Science Foundation, http://vision.middlebury.edu/stereo/data/scenes2005/
[19] Middlebury College, Microsoft Research, and the National Science Foundation, http://vision.middlebury.edu/stereo/data/scenes2006/
[2] Anders Hast, Johan Nysjö, Andrea Marchetti; "Optimal RANSAC - towards a repeatable algorithm for Finding the optimal set", Journal of WSCG 21, (2013), 21-30.
[3] Xiao Hu, Rodriguez, Gepperth; "A multi-modal system for road detection and segmentation"; 2014 IEEE Intelligent Vehicles Symposium Proceedings; Michigan, USA; 2014; 1365-1370.
[4] Atsushi Sakai, Yuya Tamura, Yoji Kuroda; "Visual odometry using feature point and ground plane for urban environment"; Robotics (ISR), 41st International Symposium on and 2010 6th German Conference on Robotics (ROBOTIK); Munich, Germany; 2010; 1-8.
[5] Tarsha-Kurdi, F., Landes, T., & Grussenmeyer, P.; "Hough-transform and extended ransac algorithms for automatic detection of 3d building roof planes from lidar data"; Proceedings of the ISPRS Workshop on Laser Scanning, Vol. 36; Espoo, Finland; 2007; 407-412.
[6] Borrmann, D., Elseberg, J., Lingemann, K., Nuchter, A.."The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design", Journal 3D Research 2(2), (2011), 1-13.
[7] Ogundana, O. O., Coggrave, C. R., Burguete, R. L., Huntley, J. M.,"Automated detection of planes in 3-D point clouds using fast Hough transforms", Optical Engineering 50(5), (2011), 53609-053609.
[8] Zhongli Wang, Jie Zhao; "Optical flow based plane detection for mobile robot navigation"; In Proceedings of the 8th World Congress on Intelligent Control and Automation; Taiwan; 2011; 1156-1160.
[9] Arshad Jamal et al; "Real-time ground plane segmentation and obstacle detection for mobile robot navigation"; 2010 International Conference on Emerging Trends in Robotics and Communication Technologies (INTERACT 2010); Chennai, India; 2010; 314-317.
[10] J. Arro'spide L. Salgado M. Nieto R.. Mohedano, "Homography-based ground plane detection using a single on-board camera", IET Intell. Transp. Syst. 4(2), (2010), 149-160.
[11] N. Mostof, M.Elhabiby, N. El-Sheimy; "Indoor localization and mapping using camera and inertial measurement unit (IMU)"; 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014; Monterey, CA, USA; 2014; 1329-1335.
[12] Prabhakar Mishra et al; "Monocular vision based real-time exploration in autonomous rovers"; 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI); Mysore, India; 2013; 42-46.
[13] K. Gong and R. Green; "Ground-plane detection using stereo depth values for wheelchair guidance"; In 24th International Conference Image and Vision Computing New Zealand (IVCNZ); Wellington, New Zealand: 2009; 97-101.
[14] Dai Yiruo, Wang Wenjia, and Kawamata Yukihiro; "Complex ground plane detection based on V-disparity map in off-road environment"; IEEE Intelligent Vehicles Symposium (IV); Gold Coast, Queensland, Australia; 2013; 1137-1142.
[15] CheeWay Teoh, ChingSeong Tan, Yong Chai Tan; "Ground plane detection for autonomous vehicle in rainforest terrain"; IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT); Wellington, New Zealand; 2010; 7-12.
[16] Nguyen Tien Dzung et al; "Gradient depth map based ground plane detection for mobile robot applications"; 8th Asian Conference on Intelligent Information and Database Systems ACIIDS 2016, Part 1, LNAI 9621; Da Nang, Vietnam; 2016: 721-730.
[17] Wikimedia Foundation, Inc. https://en.wikipedia.org/wiki/Kinect.
[18] Middlebury College, Microsoft Research, and the National Science Foundation, http://vision.middlebury.edu/stereo/data/scenes2005/
[19] Middlebury College, Microsoft Research, and the National Science Foundation, http://vision.middlebury.edu/stereo/data/scenes2006/