Robust and Efficient Slam Via Compressed H∞ Filtering
Simultaneous localization and mapping (SLAM) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the robot and features of the environment need to be estimated at the same time. In particular, it is desirable to achieve robustness and efficien...
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| Published in | Asian journal of control Vol. 16; no. 3; pp. 878 - 889 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Blackwell Publishing Ltd
01.05.2014
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1561-8625 1934-6093 |
| DOI | 10.1002/asjc.753 |
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| Abstract | Simultaneous localization and mapping (SLAM) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the robot and features of the environment need to be estimated at the same time. In particular, it is desirable to achieve robustness and efficiency in the SLAM implementation. Robots in unknown environment are likely to be subject to modeling errors which cannot be easily characterized in terms of statistical properties. To mitigate the effect of uncertainties and disturbances, robust filters such as the H∞ filter can be employed. However, robust filters are complex to implement, demanding a significant amount of computational resources. This study proposes a compressed H∞ filter to solve the robust SLAM problem in which robot dynamics are subject to uncertainties and measurements are subject to bounded‐but‐unknown disturbances. To achieve an efficient implementation, the state is partitioned into active and inactive states where the latter refers to state variables which are invariant and independent of the measurement at the epoch. With such a partitioning, the active state represents the robot pose and locations of landmarks inside a certain area surrounding the robot. The computational load is reduced since only active state needs to be estimated within a time segment. An update scheme is proposed to refine the whole state at the end of the time segment. Moreover, the relative landmark representation which results in a small cross‐correlation between active state and inactive state is employed to reduce the errors. As a result, both efficiency and robustness can be achieved. Simulations reveal that the results obtained from the proposed compressed H∞ filter, which has lower computational complexity, are very close to those from the full order H∞ filter. Further, the compressed H∞ filter is more robust than EKFs and FastSLAM. |
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| AbstractList | Simultaneous localization and mapping (
SLAM
) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the robot and features of the environment need to be estimated at the same time. In particular, it is desirable to achieve robustness and efficiency in the
SLAM
implementation. Robots in unknown environment are likely to be subject to modeling errors which cannot be easily characterized in terms of statistical properties. To mitigate the effect of uncertainties and disturbances, robust filters such as the
H
∞
filter can be employed. However, robust filters are complex to implement, demanding a significant amount of computational resources. This study proposes a compressed
H
∞
filter to solve the robust
SLAM
problem in which robot dynamics are subject to uncertainties and measurements are subject to bounded‐but‐unknown disturbances. To achieve an efficient implementation, the state is partitioned into active and inactive states where the latter refers to state variables which are invariant and independent of the measurement at the epoch. With such a partitioning, the active state represents the robot pose and locations of landmarks inside a certain area surrounding the robot. The computational load is reduced since only active state needs to be estimated within a time segment. An update scheme is proposed to refine the whole state at the end of the time segment. Moreover, the relative landmark representation which results in a small cross‐correlation between active state and inactive state is employed to reduce the errors. As a result, both efficiency and robustness can be achieved. Simulations reveal that the results obtained from the proposed compressed
H
∞
filter, which has lower computational complexity, are very close to those from the full order
H
∞
filter. Further, the compressed
H
∞
filter is more robust than
EKF
s and
FastSLAM
. Simultaneous localization and mapping (SLAM) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the robot and features of the environment need to be estimated at the same time. In particular, it is desirable to achieve robustness and efficiency in the SLAM implementation. Robots in unknown environment are likely to be subject to modeling errors which cannot be easily characterized in terms of statistical properties. To mitigate the effect of uncertainties and disturbances, robust filters such as the H∞ filter can be employed. However, robust filters are complex to implement, demanding a significant amount of computational resources. This study proposes a compressed H∞ filter to solve the robust SLAM problem in which robot dynamics are subject to uncertainties and measurements are subject to bounded‐but‐unknown disturbances. To achieve an efficient implementation, the state is partitioned into active and inactive states where the latter refers to state variables which are invariant and independent of the measurement at the epoch. With such a partitioning, the active state represents the robot pose and locations of landmarks inside a certain area surrounding the robot. The computational load is reduced since only active state needs to be estimated within a time segment. An update scheme is proposed to refine the whole state at the end of the time segment. Moreover, the relative landmark representation which results in a small cross‐correlation between active state and inactive state is employed to reduce the errors. As a result, both efficiency and robustness can be achieved. Simulations reveal that the results obtained from the proposed compressed H∞ filter, which has lower computational complexity, are very close to those from the full order H∞ filter. Further, the compressed H∞ filter is more robust than EKFs and FastSLAM. Simultaneous localization and mapping (SLAM) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the robot and features of the environment need to be estimated at the same time. In particular, it is desirable to achieve robustness and efficiency in the SLAM implementation. Robots in unknown environment are likely to be subject to modeling errors which cannot be easily characterized in terms of statistical properties. To mitigate the effect of uncertainties and disturbances, robust filters such as the H[infin] filter can be employed. However, robust filters are complex to implement, demanding a significant amount of computational resources. This study proposes a compressed H[infin] filter to solve the robust SLAM problem in which robot dynamics are subject to uncertainties and measurements are subject to bounded-but-unknown disturbances. To achieve an efficient implementation, the state is partitioned into active and inactive states where the latter refers to state variables which are invariant and independent of the measurement at the epoch. With such a partitioning, the active state represents the robot pose and locations of landmarks inside a certain area surrounding the robot. The computational load is reduced since only active state needs to be estimated within a time segment. An update scheme is proposed to refine the whole state at the end of the time segment. Moreover, the relative landmark representation which results in a small cross-correlation between active state and inactive state is employed to reduce the errors. As a result, both efficiency and robustness can be achieved. Simulations reveal that the results obtained from the proposed compressed H[infin] filter, which has lower computational complexity, are very close to those from the full order H[infin] filter. Further, the compressed H[infin] filter is more robust than EKFs and FastSLAM. [PUBLICATION ABSTRACT] |
| Author | Pham, Viet-Cuong Juang, Jyh-Ching |
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| Cites_doi | 10.1109/9.67291 10.1109/70.938382 10.1109/78.782219 10.1109/70.938381 10.1109/TRO.2008.924946 10.1109/IROS.2006.281644 10.1007/s10846‐008‐9306‐6 10.1002/asjc.526 10.1016/j.robot.2009.09.009 10.1109/IROS.2005.1545284 10.1002/asjc.322 10.1109/CDC.1992.371384 10.1137/1.9781611970760 10.1002/asjc.032 10.1016/S0947-3580(97)70089-X 10.1007/3-540-36268-1_17 10.1109/CACSD‐CCA‐ISIC.2006.4776914 10.1109/ICNC.2008.791 |
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| References_xml | – reference: West, M. E., and V. L. Syrmos, "Navigation of an autonomous underwater vehicle using robust SLAM," Proc. IEEE Int. Conf. Control Applicat., Munich, Germany, pp. 1801-1806 (2006). DOI: 10.1109/CACSD-CCA-ISIC.2006.4776914. – reference: Hassibi, B., A. H. Sayed, and T. Kailath, Indefinite-quadratic Estimation and Control - A Unified Approach to H2 and H∞ Theories, SIAM, Philadelphia, PA (1999). – reference: Yang, H. J., P. Shi, J. H. Zhang, and J. Q. Qiu, "Robust H∞ filtering for a class of Markovian jump systems with time-varying delays based on delta operator approach," Asian J. Control, Vol. 13, No. 3, pp. 398-407 (2011). – reference: An, X. Y., W. H. Zhang, and Q. H. Li, "Robust H∞ filtering of stochastic time-delay systems with state dependent noise," Asian J. Control, Vol. 10, No. 3, pp. 384-391 (2008). DOI: 10.1002/asjc.032. – reference: Zheng, Y., G. Feng, and J. B. Qiu, "Exponential H∞ filtering for discrete-time switched state-delay systems under asynchronous switching," Asian J. Control, Vol. 15, No. 2, pp. 479-488 (2013). – reference: Bolzern, P., P. Colaneri, and G. D. Nicolao, "Transient and asymptotic analysis of discrete-time H∞-filters," Eur. J. Control, Vol. 3, No. 1, pp. 317-324 (1997). – reference: Choi, J., S. Ahn, and W. K. Chung, "Robust sonar feature detection for the SLAM of mobile robot," Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Alberta, Canada, pp. 3415-3420 (2005). DOI: 10.1109/IROS.2005.1545284. – reference: Guivant, J. E., and E. M. Nebot, "Optimization of the simultaneous localization and map-building algorithm for real-time implementation," IEEE Trans. Robot. Autom., Vol. 17, No. 3, pp. 242-257 (2001). DOI: 10.1109/70.938382. – reference: Nemra, A. and N. Aouf, "Robust airborne 3D visual simultaneous localization and mapping with observability and consistency analysis," J. Intell. Robot Syst., Vol. 55, No. 4-5, pp. 345-376 (2009). DOI: 10.1007/s10846-008-9306-6. – reference: Bailey, T., J. Nieto, J. Guivant, M. Stevens, and E. Nebot, "Consistency of the EKF-SLAM algorithm," Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Beijing, China, pp. 3562-3568 (2006). DOI: 10.1109/IROS.2006.281644. – reference: Einicke, G. A. and L. B. White, "Robust extended Kalman filtering," IEEE Trans. Signal Process., Vol. 47, No. 9, pp. 2596-2599 (1999). DOI: 10.1109/78.782219. – reference: Hochdorfer, S. and C. Schlegel, "Towards a robust visual SLAM approach: Addressing the challenge of life-long operation," Proc. Int. Conf. Adv. Robot., Munich Germany, pp. 1-6 (2009). – reference: Nagpal K. M., and P. P. Khargonekar, "Filtering and smoothing in an H∞ setting," IEEE Trans. Autom. Control, Vol. 36, No. 2, pp. 152-166 (1991). DOI: 10.1109/9.67291. – reference: Green, M. and D. J. N. Limebeer, Linear Robust Control, Prentice-Hall, Englewood Cliffs, NJ (1995). – reference: Montemerlo, M., S. Thrun , D. Roller, and B. Wegbreit, "FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges," Proc. IJCAI Int. Joint Conf. Artif. Intell., Acapulco, Mexico, pp. 1151-1156 (2003). – reference: Shaked U., and Y. Theodor, "H∞-optimal estimation: A tutorial," Proc. 31st IEEE Conf. Decis. Control, Tucson, Arizona, Vol. 2, pp. 2278-2286 (1992). DOI: 10.1109/CDC.1992.371384. – reference: Kim, C., R. Sakthivel, and W. K. Chung, "Unscented FastSLAM: A robust and efficient solution to the SLAM Problem," IEEE Trans. Robot., Vol. 24, No. 4, pp. 808-820 (2008). DOI: 10.1109/TRO.2008.924946. – reference: Wurm, K. M., C. Stachniss, and G. Grisetti, "Bridging the gap between feature- and grid-based SLAM," Robot. Auton. Syst., Vol. 58, No. 2, pp. 140-148 (2010). DOI: 10.1016/j.robot.2009.09.009. – reference: Doh, N. L., W. K. 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| Snippet | Simultaneous localization and mapping (SLAM) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the... Simultaneous localization and mapping ( SLAM ) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the... |
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| SubjectTerms | compressed filter Control systems H∞ filter robotics Robots robustness SLAM |
| Title | Robust and Efficient Slam Via Compressed H∞ Filtering |
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