Self localization of an autonomous robot: Using an EKF to merge odometry and vision based landmarks

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

Armando Jorge Sousa (Author) FEUP; Paulo Gomes da Costa (Author) FEUP; António Paulo Moreira (Author) FEUP; Adriano Carvalho (Author) FEUP

Conference proceedings International

Title: ETFA 2005: 10th IEEE International Conference on Emerging Technologies and Factory Automation, Vol 1, Pts 1 and 2, Proceedings

Pages: 227-233

10th IEEE International Conference on Emerging Technologies and Factory Automation

Catania, ITALY, SEP 19-22, 2005

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-000-5ND

Abstract (EN): Localization is essential to modern autonomous robots in order to enable effective completion of complex tasks over possibly large distances in low structured environments. In this paper, a Extended Kalman Filter is used in order to implement self-localization. This is done by merging odometry and localization information, when available. The used landmarks are colored poles that can be recognized while the robot moves around performing normal tasks. This paper models measurements with very different characteristics in distance and angle to markers and shows results of the self-localization method. Results of simulations and real robot tests are shown.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7