Pregled bibliografske jedinice broj: 198382
Experimental Evaluation of Occupancy Grid Map Improvement by Sonar Data Corrections
Experimental Evaluation of Occupancy Grid Map Improvement by Sonar Data Corrections // Proceedings of the 2005 IEEE International Symposium on Intelligent Control and 2005 Mediterranean Conference on Control and Automation
Limassol, 2005. str. 95-100 doi:10.1109/.2005.1466998 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 198382 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Experimental Evaluation of Occupancy Grid Map Improvement by Sonar Data Corrections
Autori
Ivanjko, Edouard ; Petrović, Ivan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 2005 IEEE International Symposium on Intelligent Control and 2005 Mediterranean Conference on Control and Automation
/ - Limassol, 2005, 95-100
ISBN
0-7803-8936-0
Skup
IEEE International Symposium on Intelligent Control ISIC'05 and Mediterranean Conference on Control and Automation MED'05
Mjesto i datum
Limassol, Cipar, 27.06.2005. - 29.06.2005
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Bayes rule ; Dempster-Shafer rule ; Occupancy grid maps
(Bajesovo pravila ; Dempster-Shafer pravilo ; mrežaste karte zauzeća prostora)
Sažetak
In order to perform useful tasks the mobile robot's current pose must be accurately known. Problem of finding and tracking the mobile robot's pose is called localization, and can be global or local. In this paper we address local localization or mobile robot pose tracking with prerequisites of known starting pose, robot kinematic and world model. Pose tracking is mostly based on odometry, which has the problem of accumulating errors in an unbounded fashion. To overcome this problem sensor fusion is commonly used. This paper describes two methods for calibrated odometry and sonar sensor fusion based on Kalman filter theory and occupancy grid maps as used world model. Namely, we compare the pose tracking or pose estimation performances of both most commonly used nonlinear-model based estimators: extended and unscented Kalman filter. Since occupancy grid maps are used, only sonar range measurement uncertainty has to be considered, unlike feature based maps where an additional uncertainty regarding the feature/range reading assignment must be considered. Thus the numerical complexity is reduced. Experimental results on the Pioneer 2DX mobile robot show similar and improved accuracy for both pose estimation techniques compared to simple odometry.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
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Časopis indeksira:
- Scopus