Rank Kalman Filter-SLAM for Vehicle with Non-Gaussian Noise
| Autor: | Tai-Shan Lou, Su-Na Zhao, Zhen-Dong He, Ying Wang, Hong-Ye Ban |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Rank (linear algebra) ComputingMethodologies_SIMULATIONANDMODELING GeneralLiterature_INTRODUCTORYANDSURVEY Computer science Gaussian Mobile robot 02 engineering and technology Kalman filter Simultaneous localization and mapping 021001 nanoscience & nanotechnology Autonomous robot Computer Science::Robotics symbols.namesake 020901 industrial engineering & automation Gaussian noise symbols Probability distribution 0210 nano-technology Algorithm |
| Zdroj: | ICARM |
| DOI: | 10.1109/icarm49381.2020.9195299 |
| Popis: | Simultaneous localization and mapping (SLAM) is a crucial problem to solve the navigation and positioning for an autonomous robot moving in an unknown environment. This work proposes a rank Kalman filter (RKF) SLAM algorithm based on the principle of rank statistic to deal with the robot SLAM. The RKF algorithm can effectively simulate the probability distribution of nonlinear systems by using even rank sample points. The proposed RKF-SLAM is validated in simulations by comparing with two nonlinear filtering SLAM algorithms under the situation that the observation noises distribute to Gaussian distribution or non-Gaussian distribution. |
| Databáze: | OpenAIRE |
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