Authors:
Amir Monjazeb
1
;
Jurek Z. Sasiadek
1
and
Dan Necsulescu
2
Affiliations:
1
Carleton University, Canada
;
2
Ottawa University, Canada
Keyword(s):
Simultaneous Localization and Mapping (SLAM) Problem, Unscented HybridSLAM, Unscented Kalman Filter, Process Time, Root Mean Square (RMS) Position Error, Orientation Error.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Mobile Robots and Autonomous Systems
;
Modeling, Simulation and Architectures
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper presents an investigation on the performance of Unscented HybridSLAM using two different mapping strategies. The global map estimation using Unscented Kalman Filter is scrutinized for different scenarios, with and without the influence of a data association process. The accuracy of generated global map under different vehicle speed settings and with different process time is demonstrated using computer simulation. Results are discussed in terms of Root Mean Square (RMS) position error, orientation error, and time of navigation process. Results show that depending on the application, and on a desired speed, a compromise has to be done to get the best efficacy.