Localization of Mobile Robot Using Smooth Two-Part Kalman Filter
Subject Areas : electrical and computer engineering
1 - دانشگاه بیرجند
2 -
Keywords: Mobile robot, Kalman filter, two-part Kalman filter, robot localization,
Abstract :
The most important issue for a mobile robot is orientation. Success in localization is one of the four main needs in orientation, which include: perception, localization, recognition and movement control. How to provide an accurate localization solution for mobile robots is essential in many IoT applications. To achieve this goal, in this article, a method based on two-part Kalman filter is proposed for localization of mobile robot. The proposed algorithm consists of two parts, the first part is statistical linear regression and the second part is a Kalman filter with state error vector. The proposed method is tested in comparison with the new hybrid TLNF/UK method on circular, rectangular and z-shaped motion paths that are accompanied by noise. The experimental results show that the proposed method has been able to achieve better localization accuracy and it is also observed that the estimation errors in the proposed method are less and it has been able to increase the estimation accuracy compared to the combined TLNF/UK method.
[1] D. Pramod, "Robotic process automation for industry: adoption status, benefits, challenges and research agenda," Bench-Marking: An Int. J., vol. 29, no. 5, pp. 1141-1148, May 2021.
[2] S. Tomazic, "Indoor positioning and navigation," Sensors (Basel), vol. 21, no. 14, Article: 4793, Jul. 2021.
[3] C. Wang, A. Xu, J. Kuang, X. Sui, Y. Hao, and X. Niu, "A high-accuracy indoor localization system and applications based on tightly coupled UWB/INS/floor map integration," J. IEEE Sens, vol. 21, no. 16, pp. 18166-18177, 15 Aug. 2021.
[4] Y. Zhuang, J. Yang, L. Qi, Y. Li, Y. Cao, and N. El-Sheimy, "A pervasive integration platform of low-cost MEMS sensors and wireless signals for indoor localization," IEEE Internet of Things J., vol. 5, no. 6, pp. 4616-4631, Dec. 2017.
[5] Y. Yu, R. Chen, L. Chen, W. Li, and Y. Wu, "Autonomous 3D indoor localization based on crowdsourced Wi-Fi fingerprinting and MEMS sensors," J. IEEE Sens, vol. 22, no. 6, pp. 5248-5259, 15 Mar. 2021.
[6] L. Chen, X. Zhou, F. Chen, L. L. Yang, and R. Chen, "Carrier phase ranging for indoor positioning with 5G NR signals," J. IEEE Internet Things, vol. 9, no. 13, pp. 10908-10919, 1 Jul. 2021.
[7] R. Chen, et al., "Precise indoor positioning based on acoustic ranging in smartphone," IEEE Trans. Instrum, Meas., vol. 70, Article ID: 9509512, 2021.
[8] J. Li, et al. "PSOTrack: a RFID-based system for random moving objects tracking in unconstrained indoor environment," IEEE Internet of Things J., vol. 5, no. 6, pp. 4632-4641, Dec. 2018.
[9] Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, and Y. Cao, "A survey of positioning systems using visible LED lights," IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 1963-1988, Third quarter 2018.
[10] R. Garcia, H. Kuga, and W. Silva, "Unscented kalman filter and smoothing applied to attitude estimation of artificial satellites," Computational and Applied Mathematics, vol. 37, no. 4, pp. 55-64, 2018.
[11] P. Balenzuela, et al. "Accurate Gaussian mixture model smoothing usinga two-filter approach," in Proc. of the IEEE Conf. on Decision and Control, pp. 694-699, Miami Beach, FL, USA, 17-18 Dec. 2018.
[12] F. Deng, H. L. Yang, and L. J. Wang, "Adaptive unscented kalman filter based estimation and filtering for dynamic positioning with model uncertainties," J. of Control, Automation and Systems, vol. 17, no. 1, pp. 667-678, Feb. 2019.
[13] L. Dang, W. Wang, and B. Chen, "Square root unscented kalman filter with modified measurement for dynamic state estimation of power systems," IEEE Trans. on Instrumentation and Measurement, vol. 71, Article ID: 9002213 2022.
[14] A. Tuveri, F. Pérez-García, P. A. Lira-Parada, L. Imsland, and N. Bar, "Sensor fusion based on extended and unscented kalma filter for bioprocess monitoring," J. of Process Control, vol. 106, pp. 195-207, Oct. 2021.
[15] M. N. Lv, T. Sun, and J. Li, "Estimation of vehicle state parameters based on extended kalman filter," Agricultural Equipment and Vehicle Engineering, vol. 56, no. 5, pp. 77-80, 2019.
[16] A. Varsi, S. Maskell, and P. G. Spirakis, "An O(log2N) fully-balanced resampling algorithm for particle filters on distributed memory architectures," Algorithms, vol. 14, no. 12, Article ID: 342, 2021.
[17] L. Yuan, J. Gu, H. Wen, and Z. Jin, "Improved particle filter for non-gaussian forecasting-aided state estimation," J. of Modern Power Systems and Clean Energy, vol. 11, no. 4, pp. 1075-1085, Jul. 2023.
[18] A. Alessandri, T. Parisini, and R. Zoppoli, "Neural approximators for nonlinear finite-memory state estimation," Int. J. Control, vol. 67, no. 2, pp. 275-302, Jan. 1997.
[19] P. S. Kim, E. H. Lee, M. S. Jang, and S. Y. Kang, "A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay," Int. J. Distrib. Sensor Netw, vol. 13, no. 1, 8 pp., Jan. 2017.
[20] A. Jazwinski, "Limited memory optimal filtering," IEEE Trans. Autom. Control, vol. 13, no. 5, pp. 558-563, Oct. 1968.
[21] W. H. Kwon and S. Han, Receding Horizon Control: Model Predictive Control for State Models, Cham, Switzerland: Springer, 2015.
[22] C. K. Ahn, Y. S. Shmaliy, and S. Zhao, "A new unbiased FIR filter with improved robustness based on frobenius norm with exponential weight," IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 65, no. 4, pp. 521-525, Apr. 2018.
[23] Y. S. Shmaliy, "An iterative kalman-like algorithm ignoring noise and initial conditions," IEEE Trans. Signal Process., vol. 59, no. 6, pp. 2465-2473, Jun. 2011.
[24] Y. S. Shmaliy, S. H. Khan, S. Zhao, and O. Ibarra-Manzano, "General unbiased FIR filter with applications to GPS-based steering of oscillator frequency," IEEE Trans. Control Syst. Technol, vol. 25, no. 3, pp. 1141-1148, May 2017.
[25] S. L. Sun and Z. L. Deng, "Multi-sensor optimal information fusion kalman filter," Automatica, vol. 40, no. 6, pp. 1017-1023, Jun. 2004.
[26] G. Hao, S. L. Sun, and Y. Li, "Nonlinear weighted measurement fusion unscented kalman filter with asymptotic optimality," Inf. Sci., vol. 299, pp. 85-98, Apr. 2015.
[27] G. Hao and S. Sun, "Distributed fusion cubature kalman filters for nonlinear systems," Int. J., vol. 29, no. 17, pp. 5979-5991, 25 Nov. 2019.
[28] S. Sun, H. Lin, J. Ma, and X. Li, "Multi-sensor distributed fusion estimation with applications in networked systems: a review paper," Inf. Fusion, vol. 38, pp. 122-134, Nov. 2017.
[29] Y. Hu, S. Bian, B. Ji, and J. Li, "GNSS spoofing detection technique using fraction parts of double difference carrier phases," J. of Navigation, vol. 71, no. 5, pp. 1111-1129, 2018.
[30] B. S. Çiftler, S. Dikmese, İ. Güvenç, K. Akkaya, and A. Kadri, "Occupancy counting with burst and intermittent signals in smart buildings," IEEE Internet of Things J., vol. 5, no. 2, pp. 724-735, Apr. 2017.
[31] Q. Sun, Y. Tian, and M. Diao, "Cooperative localization algorithm based on hybrid topology architecture for multiple mobile robot system," IEEE Internet of Things J., vol. 5, no. 6, pp. 4753-4763, Dec. 2018.
[32] W. Ye, J. Li, J. Fang, and X. Yuan, "EGP-CDKF for performance improvement of the SINS/GNSS integrated system," IEEE Trans. on Industrial Electronics, vol. 65, no. 4, pp. 3601-3609, Apr. 2017.
[33] R. Zhan and J. Wan, "Iterated unscented kalman filter for passive target tracking," Aerospace & Electronic Systems IEEE Trans. on, vol. 43, no. 3, pp. 1155-1163, Jul. 2007.
[34] W. Li and Y. Jia, "Location of mobile station with maneuvers using an IMM-based cubature kalman filter," IEEE Trans. on Industrial Electronics, vol. 59, no. 11 pp. 4338-4348, Nov. 2012.
[35] N. K. Singh, S. Bhaumik, and S. Bhattacharya, "Tracking of ballistic target on re-entry using ensemble kalman filter," in Proc. 2012 Annual IEEE India Conf., pp. 508-513, Kochi, India, 7-9 Dec. 2012.
[36] J. Yu, J. G. Lee, G. P. Chan, and H. S. Han, "An offline navigation of a geometry PIG using a modified nonlinear fixed-interval smoothing filter," Control Engineering Practice, vol. 13, no. 3, pp. 1403-1411, Nov. 2005.
[37] Y. Xu, X. Chen, and Q. Li, "Autonomous integrated navigation for indoor robots utilizing online iterated extended rauch-tung-striebel smoothing," Sensors, vol. 13, no. 12, pp. 15937-15953, 2013.
[38] A. S. Paul and E. A. Wan, "RSSI-based indoor localization and tracking using sigma-point Kalman smoothers," IEEE J. of Selected Topics in Signal Processing, vol. 3, no. 5, pp. 860-873, Oct. 2009.
[39] R. V. D. Merwe, Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models, Oregon Health & Science University, Ph.D. Theses, Apr. 2004.
[40] X. Gong, J. Zhang, and J. Fang, "A modified nonlinear two-filter smoothing for high-precision airborne integrated GPS and inertial navigation," IEEE Trans. on Instrumentation & Measurement, vol. 64, no. 12, pp. 3315-3322, Dec. 2015.
[41] Z. Lu, J. Li, J. Fang, S. Wang, and S. Zou, "Adaptive unscented two-filter smoother applied to transfer alignment for ADPOS," IEEE Sensors J., vol. 18, no. 8, pp. 3410-3418, 15 Apr. 2018.
[42] H. Liu, K. Yang, and Q. Yang, Y. Ma, and C. Huang, "Sequential geoacoustic inversion and source tracking using ensemble Kalman-particle filter," in Proc. Global Oceans 2020: Singapore – U.S. Gulf Coast, 4- pp., Biloxi, MS, USA, 5-30 Oct. 2020.
[43] M. Murata and K. Isao, "Degeneracy-free particle filter: ensemble kalman smoother multiple distribution estimation filter," IEEE Trans. on Automatic Control, vol. 67, no. 12, pp. 6956-6961, Dec. 2022.
[44] H. M. Wu, M. Karkoub, and C. L. Hwang, "Mixed fuzzy sliding-mode tracking with backstepping formation control for multi-nonholonomic mobile robots subject to uncertainties," J. Intell. Robotic Syst., vol. 79, no. 1, pp. 73-86, Jul. 2015.
[45] Y. Eun Kim, H. Ho Kang, and C. Ki Ahn, "Two-layer nonlinear FIR filter and unscented Kalman filter fusion with application to mobile robot localization," IEEE Access, vol. 8, pp. 87173-87183, 2020.
