SLAM estimation method for uncertain model noise parameters
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It is difficult to accurately obtain the statistical parameters of mobile robots motion parameters for its mobility. And it is also the same in observation system because of the environmental variability, which would cause the SLAM system noise statistical parameters uncertainty, and decrease the filtering performance. A SLAM adaptive filtering algorithm with noise statistical characteristics estimation is proposed, when the noise statistical parameters are constant but unknown, three additional maximum posteriori estimators are constructed, and adaptive CKF SLAM algorithm are designed. Compared with the standard CKF SLAM algorithm, the results show that under the uncertainty noise parameters, if the noise variance is Gaussian white noise distribution, the designed adaptive CKF SLAM with three maximum posterior can well estimate the state, track noise variance, and handle the problem of nonlinear systems. The simulation results verify the effectiveness of the proposed SLAM estimation method with the uncertainty of the model noise parameters.
KeywordsSLAM Uncertain model Adaptive filter Noise statistics characteristic estimation
This work was supported by National Natural Science Foundation of China (Program No.11747119), and supported by the Program for Innovative Science and Research Team of Xi’an Technological University.
- 3.Zhou, W., Qiao, X., Ji, Y., et al.: An innovation and residual-based adaptive ukf algorithm. J. Astronaut. 31(7), 1798–1804 (2010)Google Scholar
- 7.Yang, Z., Pan, J., Cai, L.: Adaptive clock skew estimation with interactive multi-model Kalman filters for sensor networks. In: IEEE International Conference on Communications (ICC), 2010, 1–5 (2010)Google Scholar
- 8.Tudoroiu, N., Khorasani, K.: Fault detection and diagnosis for satellite’s attitude control system (ACS) using an interactive multiple model (IMM) approach. In: Proceedings of 2005 IEEE Conference on Control Applications. 1287–1292 (2005)Google Scholar
- 9.Yu, H., Song, S., Wang, S.: Interaction cubature Kalman filter and its application. Control Decis. 30(09), 1660–1666 (2015)Google Scholar
- 11.Ying-chun, S.: Current statistics model and adaptive kalman filter in gps navigation. J. Hunan Inst. Hum. Sci. Technol. 05, 12–14 (2005)Google Scholar
- 14.Julier, S.J.: The scaled unscented transformation. American Control Conference, 2002. In: Proceedings of the 2002. IEEE, 6, 4555–4559 (2002)Google Scholar