期刊
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
卷 19, 期 4, 页码 1677-1690出版社
INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
DOI: 10.1007/s12555-019-0997-1
关键词
FastSLAM; genetic algorithm; particle filter; simultaneous localization and mapping (SLAM); time varying noise estimator; unscented Kalman filter
资金
- National Natural Science Foundation of China [61771091,61871066]
- National High Technology Research and Development Program (863 Program) of China [2015AA016306]
- Natural Science Foundation of Liaoning Province of China [20170540159]
- Fundamental Research Funds for the Central Universities of China
This paper proposes an improved adaptive unscented FastSLAM algorithm, which enhances system tracking ability by using an adaptive factor and constructing a Huber cost function, resampling in particle filter with an improved genetic algorithm to complete robot tracking, achieving good tracking performance and reliable state estimation.
The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, an improved adaptive unscented FastSLAM with genetic resampling is proposed. Specifically, the adaptive unscented Kalman filter (IAUKF) algorithm is improved as importance sampling of particle filter, where the adaptive factor is used to improve the tracking ability of system and the Huber cost function is constructed to decrease the measurement covariance. Next, the process noise and the measurement noise are assessed by a time varying estimator. Moreover, the resampling in particle filter is carried out by an improved genetic algorithm (GA). Finally, the improved adaptive unscented FastSLAM (IAUFastSLAM) is proposed to complete robot tracking. The proposed algorithm has good tracking performance and obtains reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.
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