29 April 2016 Automatic detection and classification of obstacles with applications in autonomous mobile robots
Author Affiliations +
Abstract
Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Volodymyr I. Ponomaryov, Volodymyr I. Ponomaryov, Dario I. Rosas-Miranda, Dario I. Rosas-Miranda, } "Automatic detection and classification of obstacles with applications in autonomous mobile robots", Proc. SPIE 9897, Real-Time Image and Video Processing 2016, 98970P (29 April 2016); doi: 10.1117/12.2227758; https://doi.org/10.1117/12.2227758
PROCEEDINGS
11 PAGES


SHARE
Back to Top