EKF-SLAM for AUV navigation under probabilistic sonar scan-matching

Angelos Mallios, Pere Ridao, David Ribas, Francesco Maurelli, Yvan Petillot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach. ©2010 IEEE.

Original languageEnglish
Title of host publicationIEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
Pages4404-4411
Number of pages8
DOIs
Publication statusPublished - 2010
Event23rd IEEE/RSJ International Conference on Intelligent Robots and Systems 2010 - Taipei, Taiwan, Province of China
Duration: 18 Oct 201022 Oct 2010

Conference

Conference23rd IEEE/RSJ International Conference on Intelligent Robots and Systems 2010
Abbreviated titleIROS 2010
CountryTaiwan, Province of China
CityTaipei
Period18/10/1022/10/10

Fingerprint

Autonomous underwater vehicles
Sonar
Extended Kalman filters
Navigation
Robots
Marinas
Acoustics
Scanning
Imaging techniques
Sensors
Uncertainty

Cite this

Mallios, A., Ridao, P., Ribas, D., Maurelli, F., & Petillot, Y. (2010). EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. In IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings (pp. 4404-4411) https://doi.org/10.1109/IROS.2010.5649246
Mallios, Angelos ; Ridao, Pere ; Ribas, David ; Maurelli, Francesco ; Petillot, Yvan. / EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings. 2010. pp. 4404-4411
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Mallios, A, Ridao, P, Ribas, D, Maurelli, F & Petillot, Y 2010, EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. in IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings. pp. 4404-4411, 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems 2010, Taipei, Taiwan, Province of China, 18/10/10. https://doi.org/10.1109/IROS.2010.5649246

EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. / Mallios, Angelos; Ridao, Pere; Ribas, David; Maurelli, Francesco; Petillot, Yvan.

IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings. 2010. p. 4404-4411.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach. ©2010 IEEE.

AB - This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach. ©2010 IEEE.

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Mallios A, Ridao P, Ribas D, Maurelli F, Petillot Y. EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. In IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings. 2010. p. 4404-4411 https://doi.org/10.1109/IROS.2010.5649246