Design Hints for Efficient Robotic Vision - Lessons Learned from a Robotic Platform

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Costa, V (Author) Other; Cebola, P (Author) Other; Armando Jorge Sousa (Author) FEUP; Ana Reis (Author) FEUP

Conference proceedings International

Title: European Congress on Computational Methods in Applied Sciences and Engineering: ECCOMAS 2017: VipIMAGE 2017

Pages: 515-524

6th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE)

Porto, PORTUGAL, OCT 18-20, 2017

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00N-5WK

DOI: 10.1007/978-3-319-68195-5_56

Abstract (EN): Interest in autonomous vehicles has steadily increased in recent years. A number of tasks, like lane tracking, semaphore detection and decoding, are key features for a self-driving robot. This paper presents a path detection and tracking algorithm using the Inverse Perspective Mapping and Hough Transform methods compounded with real-time vision techniques and a semaphore recognition system based on color segmentation. An evaluation of the proposed algorithm is performed and a comparison between the results using real-time techniques is also presented. The suggested architecture has been put to test on autonomous driving robot who competed in the Portuguese autonomous vehicle competition called "Festival Nacional de Robotica". The overall process of the lane tracking algorithm, takes about 1.4 ms per image, almost 60 times faster than the first algorithm tested and a good accuracy, showing a translation error below 0.03m and a rotation error below 5 degrees. Regarding the real-time semaphore recognition, it takes about 0.35 ms to detect a semaphore and has achieved a perfect score in the laboratory tests performed.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10