Robustified estimation algorithms for mobile robot localization based on geometrical environment maps
| Autor: | Geovany A. Borges, M.-J. Aldon |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science General Mathematics Estimator Pattern recognition Mobile robot 02 engineering and technology Kalman filter 3D pose estimation Computer Science Applications 020901 industrial engineering & automation Control and Systems Engineering Feature (computer vision) Outlier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Representation (mathematics) Algorithm Pose Software |
| Zdroj: | Robotics and Autonomous Systems. 45:131-159 |
| ISSN: | 0921-8890 |
| DOI: | 10.1016/j.robot.2003.09.003 |
| Popis: | This paper presents an improved weighted least-squares algorithm used for optimal 2D pose estimation of mobile robots navigating in real environments represented by geometrical maps. Following this map representation paradigm, feature matching is an important step in pose estimation. In this process, false feature matches may be accepted as reliable. Thus, in order to provide reliable pose estimation even in the presence of a certain level of false matches, robust M-estimators are derived. We further apply some concepts of outlier rejection for deriving a robust Kalman filter-based pose estimator. Extensive comparisons of the proposed robust methods with classic Kalman filtering-based approaches were carried out in real environments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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