Authors: D. Abraham Chandy; Biji Yohannan; A. Hepzibah Christinal; Riju Ghosh
Addresses: Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of Electrical and Electronics, Oxford Engineering College, Trichy, India ' Department of Mathematics, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
Abstract: Autonomous vehicles are used for a range of tasks, such as automated highway driving, transporting work, etc. These vehicles are used both in structured and unstructured environments. This work presents an effective method for path detection using statistical texture features extracted from fused LIDAR sensor and visual camera images. An edge-based feature detection approach is adopted for image registration. The Grey Level Co-occurrence Matrix (GLCM)-based texture features are extracted from the fused image. Classification performance of K-NN and Support Vector Machine (SVM) classifiers are analysed in this work. For experimentation, the data available in Ford Campus Vision data set are used. The results of this new approach are very promising for path detection problem of unmanned ground vehicles.
Keywords: unmanned ground vehicle; autonomous navigation; image fusion; SVM; support vector machines; ford campus vision.
International Journal of Vehicle Autonomous Systems, 2019 Vol.14 No.3, pp.265 - 277
Received: 03 Sep 2017
Accepted: 07 Oct 2018
Published online: 17 May 2019 *