Automatic detection and classification of obstacles with applications in autonomous mobile robots

Volodymyr I. Ponomaryov; Dario I. Rosas-Miranda

doi:10.1117/12.2227758

29 April 2016 Automatic detection and classification of obstacles with applications in autonomous mobile robots

Volodymyr I. Ponomaryov, Dario I. Rosas-Miranda

Proceedings Volume 9897, Real-Time Image and Video Processing 2016; 98970P (2016) https://doi.org/10.1117/12.2227758
Event: SPIE Photonics Europe, 2016, Brussels, Belgium

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.

Citation Download Citation

Volodymyr I. Ponomaryov and 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); https://doi.org/10.1117/12.2227758

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available