research-article

A new approach to improve the success and solving the UGVs Cooperation for SLAM Problem, using a SVSF Filter

Authors:

A. BazoulaAuthors Info & Claims

MedPRAI-2016: Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence

Pages 56 - 63

https://doi.org/10.1145/3038884.3038894

Published: 22 November 2016 Publication History

Abstract

This paper aims to present a Decentralized Cooperative Simultaneous Localization and Mapping (DC-SLAM) solution based on a laser telemeter using a Covariance Intersection (CI). The CI will run in the UGVs receiving features to estimate the position and covariance of shared features before adding them to the global map. With the proposed solution, a group of Unmanned Ground Vehicles (UGVs) will be able to construct a large reliable map and localize themselves within this map without any user intervention. The most popular solutions of this problem are the EKF-SLAM and the FAST-SLAM, the former suffers from two important problems, which are the calculation of Jacobeans and the linear approximations to the nonlinear models, and the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF). Cooperative SVSF-SLAM algorithm is proposed in this paper to solve the UGVs SLAM problem. Our main contribution consists in adapting the SVSF filter to solve the Decentralized Cooperative SLAM problem for multiple UGV. The algorithms developed in this paper were implemented using two mobile robots Pioneer 3-AT, using 2D laser telemeter sensors. Good results are obtained by the Cooperative Adaptive SVSF-SLAM comparing to the Cooperative EKF-SLAM especially when the noise is colored or affected by a variable bias. Simulation results confirm and show the efficiency of our proposed approaches.1

References

[1]

K. Sty. Using situated communication in distributed autonomous mobile Robots. In Proceedings of Seventh Scandinavian Conference on Artificial Intelligence (SCAI01), 2001.

[2]

A.I. Mourikis, S.I. Roumeliotis. Performance Analysis of Multi-robot Cooperative Localisation. IEEE Transactions on Robotics 22, 4 (2006), 666--681.

Digital Library

[3]

S.I. Roumeliotis, I.M. Rekleitis. Propagation of Uncertainty in Cooperative Multirobot Localisation: Analysis and Experimental Results. Autonomous Robots 17, 1 (2004), 41--54.

Digital Library

[4]

W. Burgard, D. Fox, S. run. Active mobile robot localization by entropy Minimiaation. In proceedings of Second Euro-micro Workshop on Advanced Mobile Robots (EUROBOT'97), 1997.

[5]

Rui Rochaa, b. Jorge Diasa, Adriano Carvalhob. Cooperative multi-robot systems: A study of vision-based 3--D mapping using information theory. Robotics and Autonomous Systems 53 (2005), 282--311.

[6]

A. Yamashita, T. Arai, J. Ota, and H. Asama. Motion planning of multiple mobile robots for cooperative manipulation and transportation. IEEE Transactions on Robotics and Automation 19 (2003), 223--237.

[7]

Martinelli, F. Pont, and R. Siegwart. Multi-robot localization using relative observations. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, IEEE, 1050-4729.

[8]

N. Trawny and T. Barfoot. Optimized motion strategies for cooperative localization of mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation 1 (2004), 1027--1032.

[9]

Wu, Ming and Huang, Feifei and Wang, Long and Sun, Jiyin. Cooperative Multi-Robot, Monocular-SLAM Using Salient Landmarks. In Proceedings 2009 of the International Asia Conference on Informatics in Control, Automation and Robotics, 151--155.

[10]

N. Ergin Özkucur and H. Levent Akın. 2010. Cooperative Multi-robot Map Merging Using Fast-SLAM. In Lecture on Robot SoccerWorld Cup XIII, Baltes J, Lagoudakis M.G, Naruse T and Shiry S. (Eds.). Lecture Noes in Computer on Science, Vol. 5949. Springer, Heidelberg, Berlin, 449--460.

[11]

M. W. M. G. Dissanayake, P. Newman, S. Clark, and H. F. Durrant-Whyte. Asolution to the simultaneous localization and map building (SLAM) Problem. IEEE Tran. Robot. Automat 17, 3 (2001), 229--241.

[12]

Abdelkrim. Nemra, Luis M. Bergasa, Elena Lpez, Rafael Barea, Alejandro Gmez, lvaro Saltos. Robust Visual Simultaneous Localization and Mapping for MAV Using Smooth Variable Structure Filter. In Lecture on Second Iberian Robotics Conference, Luís Paulo R, António Paulo M, Pedro U. L, Luis M and Victor M. M (Eds.). Lecture Noes in Advances in Intelligent Systems and Computing, Vol. 417. Springer, Berlin, 557--569.

[13]

D. Fox, W. Burgard, H. Kruppa, and S. run. A probabilistic approach to collaborative multi-robot localization. Autonomous Robots 8 (2000), 325--344.

Digital Library

[14]

S. run. A probabilistic online mapping algorithm for teams of mobile robots, The Int. J. Rob. Res 20, 5 (2001), 335--363.

[15]

Arturo Gil, scar Reinoso, Mnica Ballesta, Miguel Juli. Multi-robot visual SLAM using a Rao-Blackwellized particle filter. Robotics and Autonomous Systems 58 (2010), 68--80.

Digital Library

[16]

Dellaert F, and Kaess M. Square Root SAM, Simultaneous Localization And Mapping via Square Root Information Smoothing, The Journal of Robotics Research 25,12 (2006), 1181--1203.

Digital Library

[17]

Kaess.M, Ranganathan, A, and Dellaert, F. iSAM. Incremental Smoothing and Mapping. IEEE Trans. On Robotics 24, 6 (2008), 1365--1378.

[18]

Andersson, L.A.A. Nygards, J C-SAM. Multi-Robot SLAM using square root in formation smoothing. 2008 IEEE International Conference on Robotics and Automation pp, 2798--2805.

[19]

S. R. Habibi and R. Burton, e. Variable Structure Filter. The Journal of Dynamic Systems, Measurement, and Control, 125 (2003),. 287--293.

[20]

S. R. Habibi. Smooth Variable Structure Filter. In Proceedings of the IEEE 95, 5 (2007), 1026--1059.

[21]

Sfeir J. 2009. Navigation d'un Robot Mobile en Environnement I nconnu Utilisant les Champs de Potentiels Artificiels. Ph.D. Ecole de Technologie Suprieure, Montral, Canda.

[22]

P. Moutarlier and R. Chatila. Stochastic multisensory data fusion for mobile robot location and environment modeling. In International Symposium of Robotics Research (1989), 85--94.

[23]

M. Al-Shabi. 2011. General Toeplitz/Observability SVSF. Ph.D. Department of Mechanical Engineering, McMaster University, Hamilton, Ontario.

[24]

A. Nemra and N. Aouf. Robust airborne 3d visual simultaneous localization and mapping with observability and consistency analysis. Journal. Intell. Robotics Syst, 55, 4-5 (2009), 345--376.

[25]

Y. Cao, A. Fukunaga, A. Kahng. Cooperative mobile robotics: antecedents and directions, Auton. Robots 4 (1997), 1--23.

Digital Library

[26]

F. Demim, A. Nemra, K. Louadj, Z. Mehal, M. Hamelain and A. Bazoula. Simultaneous localization and mapping algorithm for unmanned ground vehicle with SVSF filter. In Proc. EEE Conference (2016), 155--162.

[27]

F. Demim, A. Nemra, K. Louadj. Robust SVSF-SLAM for Unmanned Vehicle in Unknown Environment. Journal of of IFAC-PapersOnLine ScienceDirect, 49-21 (2016), 386--394.

Cited By

Demim FRouigueb ANemra ABenghezal ABazoula ADjamaa B(2024)Improved Sliding Mode Control Technique Based on the Smoothing Boundary Layer Width for Simultaneous Localization and Mapping of Unmanned Ground VehicleProceedings of the 5th International Conference on Electrical Engineering and Control Applications–Volume 210.1007/978-981-97-4776-4_40(399-410)Online publication date: 3-Sep-2024
https://doi.org/10.1007/978-981-97-4776-4_40
Demim FAllam ARouigueb ABenghezal ANemra ABazoula AKobzili EHamerlain M(2024)Unmanned Aerial Vehicle Localization Using an Advanced Technique Based on the Smooth Variable Structure FilterProceedings of the 5th International Conference on Electrical Engineering and Control Applications–Volume 110.1007/978-981-97-0045-5_47(539-554)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-981-97-0045-5_47
Demim FBenmansour SAbdelkrim NRouigueb AHamerlain MBazoula A(2021)Simultaneous localisation and mapping for autonomous underwater vehicle using a combined smooth variable structure filter and extended kalman filterJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2021.190843034:4(621-650)Online publication date: 14-Apr-2021
https://doi.org/10.1080/0952813X.2021.1908430
Show More Cited By

Recommendations

Real-time motion planning of an autonomous mobile manipulator using a fuzzy adaptive Kalman filter

This paper presents the real-time motion planning i.e., map building and path planning of an autonomous mobile manipulator capable of scanning natural terrain using a detector e.g., a landmine detector. Map building generates a terrain map using the ...
Towards solving an obstacle problem by the cooperation of UAVs and UGVs
SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

We study a heterogeneous autonomous robot team composed of UAVs (Unmanned Air Vehicles) and UGVs (Unmanned Ground Vehicles) cooperate with each other to carry out global tasks. We introduce the system that UAVs and UGVs remove obstacles cooperatively. ...
Localization of a Mobile Autonomous Robot Using Extended Kalman Filter
ICACC '13: Proceedings of the 2013 Third International Conference on Advances in Computing and Communications

This paper demonstrates an effective method for combining measurements from a gyroscope and rotary wheel encoders (odometry) in mobile robot localization. Sensor fusion of this kind is done using an Extended Kalman filter obtained from the values of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

MedPRAI-2016: Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence

November 2016

163 pages

ISBN:9781450348768

DOI:10.1145/3038884

General Chairs:
Chawki Djeddi
Larbi Tebessi University, Algeria
,
Imran Siddiqi
Bahria University, Pakistan
,
Akram Bennour
Larbi Tebessi University, Algeria
,
Program Chairs:
Youcef Chibani
University of Science and Technology Houari Boumediene, Algeria
,
Haikal El Abed
Technical Trainers College, German International Cooperation, Kingdom of Saudi Arabia

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

MedPRAI-2016

MedPRAI-2016: Mediterranean Conference on Pattern Recognition and Artificial Intelligence

November 22 - 23, 2016

Tebessa, Algeria

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
78
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Demim FRouigueb ANemra ABenghezal ABazoula ADjamaa B(2024)Improved Sliding Mode Control Technique Based on the Smoothing Boundary Layer Width for Simultaneous Localization and Mapping of Unmanned Ground VehicleProceedings of the 5th International Conference on Electrical Engineering and Control Applications–Volume 210.1007/978-981-97-4776-4_40(399-410)Online publication date: 3-Sep-2024
https://doi.org/10.1007/978-981-97-4776-4_40
Demim FAllam ARouigueb ABenghezal ANemra ABazoula AKobzili EHamerlain M(2024)Unmanned Aerial Vehicle Localization Using an Advanced Technique Based on the Smooth Variable Structure FilterProceedings of the 5th International Conference on Electrical Engineering and Control Applications–Volume 110.1007/978-981-97-0045-5_47(539-554)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-981-97-0045-5_47
Demim FBenmansour SAbdelkrim NRouigueb AHamerlain MBazoula A(2021)Simultaneous localisation and mapping for autonomous underwater vehicle using a combined smooth variable structure filter and extended kalman filterJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2021.190843034:4(621-650)Online publication date: 14-Apr-2021
https://doi.org/10.1080/0952813X.2021.1908430
Demim FNemra AMouali OHedir MRouigueb AHamerlain MBendoumi MBazoula A(2020)Simultaneous Localization and Mapping Algorithm based on 3D Laser for Unmanned Aerial VehicleProceedings of the 4th International Conference on Electrical Engineering and Control Applications10.1007/978-981-15-6403-1_69(1003-1020)Online publication date: 30-Sep-2020
https://doi.org/10.1007/978-981-15-6403-1_69
Demim FNemra AHedir MRouigueb ABazoula AHamerlain M(2019)Real-time Application of SLAM based-line for Unmanned ground vehicle2019 6th International Conference on Image and Signal Processing and their Applications (ISPA)10.1109/ISPA48434.2019.8966910(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/ISPA48434.2019.8966910
Demim FNemra ABoucheloukh AKobzili EHamerlain MBazoula A(2019)SLAM based on Adaptive SVSF for Cooperative Unmanned Vehicles in Dynamic environmentIFAC-PapersOnLine10.1016/j.ifacol.2019.08.05152:8(73-80)Online publication date: 2019
https://doi.org/10.1016/j.ifacol.2019.08.051
Abdelghani BFares BAbdelkrim NFethi DRabah L(2018)Stereo Visual Odometry for Mobile Robot2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS.2018.8598501(1-7)Online publication date: Oct-2018
https://doi.org/10.1109/PAIS.2018.8598501
Demim FNemra AAbdelkadri HBazoula ALouadj KHamerlain M(2018)SLAM Problem for Autonomous Underwater Vehicle using SVSF Filter2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP)10.1109/IWSSIP.2018.8439195(1-5)Online publication date: Jun-2018
https://doi.org/10.1109/IWSSIP.2018.8439195
Demim FNemra ALouadj KBoucheloukh AKobzili EAllam AHamerlain MBazoula A(2018)Visual SVSF-SLAM Algorithm Based on Adaptive Boundary Layer WidthAdvanced Control Engineering Methods in Electrical Engineering Systems10.1007/978-3-319-97816-1_8(97-112)Online publication date: 11-Sep-2018
https://doi.org/10.1007/978-3-319-97816-1_8
Demim FBoucheloukh ANemra ALouadj KHamerlain MBazoula AMehal Z(2017)A new adaptive smooth variable structure filter SLAM algorithm for unmanned vehicle2017 6th International Conference on Systems and Control (ICSC)10.1109/ICoSC.2017.7958664(6-13)Online publication date: May-2017
https://doi.org/10.1109/ICoSC.2017.7958664

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents