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A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review Cover

A State of the Art in Simultaneous Localization and Mapping (SLAM) for Unmanned Ariel Vehicle (UAV): A Review

Electrical, Control and Communication Engineering
Volume 18 (2022): Issue 1 (June 2022)
By: Abdul Rauf,  Muhammad Jehanzeb Irshad,  Muhammad Wasif,  Zubair Mehmood,  Tayybah Kiren and  Nazam Siddique  
Open Access
|Jan 2023

References

  1. [1] S. Sukkarieh and M. George, “Tightly coupled INS/GPS with bias estimation for UAV applications,” in Proceedings of Australiasian Conference on Robotics and Automation (ACRA), Sydney, Australia, 2005.
    Search in Google ScholarBack to article
  2. [2] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT press, 2005. chromeextension://efaidnbmnnnibpcajpcglclefindmkaj/https://docs.ufpr.br/~danielsantos/ProbabilisticRobotics.pdf
    Search in Google ScholarBack to article
  3. [3] M. A. Sedrine, W. S. Mseddi, and R. Attia, “Neural network visual odometry based framework for UAV localization in GPS denied environment,” COMPUSOFT: An International Journal of Advanced Computer Technology, vol. 9, no. 8, pp. 3798–3809, Sep. 2020.
    Search in Google ScholarBack to article
  4. [4] H. Shakhatreh, A. Sawalmeh, A.I. Al-Fuqaha, Z. Dou, E. Almaita, I.M. Khalil, N.S. Othman, A. Khreishah, and M. Guizani, “Unmanned aerial vehicles: A survey on civil applications and key research challenges,” arXiv, 2018, arXiv:1805.00881.10.1109/ACCESS.2019.2909530
    Search in Google ScholarBack to article
  5. [5] V. Kumar and N. Michael, “Opportunities and challenges with autonomous micro aerial vehicles,” The International Journal of Robotics Research, vol. 31, no. 11, 1279–1291, Aug. 2012. https://doi.org/10.1177/027836491245595410.1177/0278364912455954
    Search in Google ScholarBack to article
  6. [6] S. Grzonka, G. Grisetti, and W. Burgard, “A fully autonomous indoor quadrotor,” IEEE Transactions on Robotics, vol. 28, no. 1, pp. 90–100, Feb. 2012. https://doi.org/10.1109/TRO.2011.216299910.1109/TRO.2011.2162999
    Search in Google ScholarBack to article
  7. [7] C. Cadena et al., “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309–1332, Dec. 2016. https://doi.org/10.1109/TRO.2016.262475410.1109/TRO.2016.2624754
    Search in Google ScholarBack to article
  8. [8] A. Ravankar, A.A. Ravankar, Y. Kobayashi, and T. Emaru, “Symbiotic navigation in multi-robot systems with remote obstacle knowledge sharing,” Sensors, vol. 17, no. 7, Art. no. 1581, Jul. 2017. https://doi.org/10.3390/s1707158110.3390/s17071581553969428678193
    Search in Google ScholarBack to article
  9. [9] E. Feron and E. N. Johnson, “Aerial robotics,” in Springer Handbook of Robotics, B. Siciliano and O. Khatib, Eds. Springer, Berlin, Heidelberg, 2008, pp. 1009–1029. https://doi.org/10.1007/978-3-540-30301-5_4510.1007/978-3-540-30301-5_45
    Search in Google ScholarBack to article
  10. [10] C. Papachristos, T. Dang, S. Khattak, F. Mascarich, N. Khedekar, and K. Alexis, “Modeling, control, state estimation and path planning methods for autonomous multirotor aerial robots,” Foundations and Trends® in Robotics, vol. 7, no. 3, 180–250, Dec. 2018. https://doi.org/10.1561/230000005810.1561/2300000058
    Search in Google ScholarBack to article
  11. [11] A. Khalajmehrabadi, N. Gatsis, and D. Akopian, “Modern WLAN fingerprinting indoor positioning methods and eployment challenges,” arXiv preprint, arXiv:1610.05424, Oct. 2016. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/1610.05424.pdf
    Search in Google ScholarBack to article
  12. [12] J. González, J.-L. Blanco, C. Galindo, A. Ortiz-de Galisteo, J.-A. Fernández-Madrigal, F. A. Moreno, and J. L. Martínez, “Mobile robot localization based on ultra-wide-band ranging: A particle filter approach,” Robotics and Autonomous Systems, vol. 57, no. 5, pp. 496–507, May 2009. https://doi.org/10.1016/j.robot.2008.10.02210.1016/j.robot.2008.10.022
    Search in Google ScholarBack to article
  13. [13] L. Jayatilleke and N. Zhang, “Landmark-based localization for unmanned aerial vehicles,” in 2013 IEEE International Systems Conference (SysCon), Orlando, FL, USA, Apr. 2013, pp. 448–451. https://doi.org/10.1109/SysCon.2013.654992110.1109/SysCon.2013.6549921
    Search in Google ScholarBack to article
  14. [14] R. Negenborn, “Robot localization and Kalman filters”, M.S. thesis, Utrecht University, Netherlands, Sep. 2003. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://www.negenborn.net/kal_loc/thesis.pdf
    Search in Google ScholarBack to article
  15. [15] S. Thrun, “Bayesian landmark learning for mobile robot localization,” Machine Learning Journal, vol. 33, pp. 41–76, Oct. 1998. https://doi.org/10.1023/A:100755453124210.1023/A:1007554531242
    Search in Google ScholarBack to article
  16. [16] J.-O. Lee, T. Kang, K.-H. Lee, S. Kyu Im, and J. Park, “Vision-based indoor localization for unmanned aerial vehicles,” Journal of Aerospace Engineering, vol. 24, no. 3, pp. 373–377, Jul. 2011. https://doi.org/10.1061/(ASCE)AS.1943-5525.000006410.1061/(ASCE)AS.1943-5525.0000064
    Search in Google ScholarBack to article
  17. [17] F. Thomas and L. Ros, “Revisiting trilateration for robot localization,” IEEE Trans. Robot., vol. 21, no. 1, pp. 93–101, Feb. 2005. https://doi.org/10.1109/TRO.2004.83379310.1109/TRO.2004.833793
    Search in Google ScholarBack to article
  18. [18] S. Se, D. Lowe, and J. Little, “Vision-based global localization and mapping for mobile robots,” IEEE Trans. Robot., vol. 21, no. 3, pp. 364–375, Jun. 2005. https://doi.org/10.1109/TRO.2004.83922810.1109/TRO.2004.839228
    Search in Google ScholarBack to article
  19. [19] F. Calabrese and G. Indiveri, “An omni-vision triangulation-like approach to mobile robot localization,” in Proc. Conf. Intell. Control, Limassol, Cyprus, Jun. 2005, pp. 604–609.10.1109/.2005.1467083
    Search in Google ScholarBack to article
  20. [20] I. Shimshoni, “On mobile robot localization from landmark bearings,” IEEE Trans. Robot. Autom., vol. 18, no. 6, pp. 971–976, Dec. 2002. https://doi.org/10.1109/TRA.2002.80566310.1109/TRA.2002.805663
    Search in Google ScholarBack to article
  21. [21] M. Li, Z. Liu, J. Huang, and K. Imou, “Landmark direction angle estimation based on omnidirectional image,” in Proc. Int. Conf. Inf. Autom., Harbin, China, Jun. 2010, pp. 1128–1133. https://doi.org/10.1109/ICINFA.2010.551231610.1109/ICINFA.2010.5512316
    Search in Google ScholarBack to article
  22. [22] K. Briechle and U. Hanebeck, “Localization of a mobile robot using relative bearing measurements,” IEEE Trans. Robot. Autom., vol. 20, no. 1, pp. 36–44, Feb. 2004. https://doi.org/10.1109/TRA.2003.82093310.1109/TRA.2003.820933
    Search in Google ScholarBack to article
  23. [23] D. Fox, W. Burgard, H. Kruppa, and S. Thrun, “Aprobabilistic approach to collaborative multi-robot localization,” Auton. Robots, vol. 8, pp. 325–344, Jun. 2000. http://dx.doi.org/10.1023/A:100893791139010.1023/A:1008937911390
    Search in Google ScholarBack to article
  24. [24] J. S. Jang, M.-J. Jung, J.-H. Kim, and J.-S. Choi, “Cooperative localization in multi-agent robotic system,” presented at the FIRARobotWorld Congr., Seoul, Korea, May 2002.
    Search in Google ScholarBack to article
  25. [25] C. Roehrig and C. Kirsch, “Particle filter based sensor fusion of range measurements from wireless sensor network and laser range finder,” in Robotics (ISR), 2010 41st International Symposium on and 2010 6th German Conference on Robotics (ROBOTIK), Jun. 2010, pp. 1–8. https://www.researchgate.net/publication/224232494_Particle_Filter_Based_Sensor_Fusion_of_Range_Measurements_from_Wireless_Sensor_Network_and_Laser_Range_Finder
    Search in Google ScholarBack to article
  26. [26] K. Baik, J. Shin, S. Ji, W. Shon, and S. Park, “A vision system for UAV position control,” in 2011 IEEE Aerospace Conference, Big Sky, MT, USA, Mar. 2011, pp. 1–6. https://doi.org/10.1109/AERO.2011.574751510.1109/AERO.2011.5747515
    Search in Google ScholarBack to article
  27. [27] M. Latroch, O. Abdelhafid, H. Koivo, and A. L. Harbo, “A radio-vision localization for small UAVs flying in swam,” IEEE Transactions on Aerospace and Electronic Systems, 2012. https://www.academia.edu/26578927/A_Radio_vision_localization_for_small_UAVs_flying_in_swam
    Search in Google ScholarBack to article
  28. [28] A. Cesetti, E. Frontoni, A. Mancini, A. Ascani, P. Zingaretti, and S. Longhi “A visual global positioning system for unmanned aerial vehicles used in photogrammetric applications,” Journal of Intelligent & Robotic Systems, vol. 61, pp. 157–168, Nov. 2010. https://doi.org/10.1007/s10846-010-9489-510.1007/s10846-010-9489-5
    Search in Google ScholarBack to article
  29. [29] Z. Kurt-Yavuz and S. Yavuz, “A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms,” in 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), Lisbon, Portugal, June 2012, pp. 37–43. https://doi.org/10.1109/INES.2012.624986610.1109/INES.2012.6249866
    Search in Google ScholarBack to article
  30. [30] B. Boots and G. J. Gordon, “A spectral learning approach to range-only SLAM,” arXiv:1207.2491, July 2012. [Online]. Available: http://arxiv.org/abs/1207.2491
    Search in Google ScholarBack to article
  31. [31] A. Kehagias, J. Djugash, and S. Singh, “Range-only SLAM with interpolated range data,” Carnegie Mellon University, Tech. Rep. CMURI- TR-06-26, May 2006. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ri.cmu.edu/pub_files/pub4/kehagias_athanasios_2006_1/kehagias_athanasios_2006_1.pdf
    Search in Google ScholarBack to article
  32. [32] F. R. Fabresse, F. Caballero, I. Maza, and A. Ollero, “Undelayed 3D ROSLAM based on gaussian-mixture and reduced spherical parametrization,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo Big Sight, Tokyo, Japan, Nov. 2013, pp. 1555–1561. https://doi.org/10.1109/IROS.2013.669655610.1109/IROS.2013.6696556
    Search in Google ScholarBack to article
  33. [33] F. Fabresse, F. Caballero, L. Merino, and A. Ollero, “Active perception for 3D Range-only Simultaneous Localization and Mapping with UAVs,” in Proceedings of the International Conference on Unmanned Aircraft Systems, ICUAS, Arlington, VA, USA, Jun. 2016, pp. 394–398. https://doi.org/10.1109/ICUAS.2016.750263910.1109/ICUAS.2016.7502639
    Search in Google ScholarBack to article
  34. [34] G. Grisetti, R. Kuemmerle, C. Stachniss, and W. Burgard, “A tutorial on graph-based SLAM,” Intelligent Transportation Systems Magazine, vol. 2, no. 4, pp. 31–43, 2010. https://doi.org/10.1109/MITS.2010.93992510.1109/MITS.2010.939925
    Search in Google ScholarBack to article
  35. [35] F. Demim, A. Nemra, K. Louadj, Z. Mehal, M. Hamerlain, and A. Bazoula, “Simultaneous localization and mapping algorithm for unmanned ground vehicle with SVSF filter,” in 2016 8th International Conference on Modelling, Identification and Control (ICMIC), Algiers, Algeria, Nov. 2016, pp. 155–162. https://doi.org/10.1109/ICMIC.2016.780429110.1109/ICMIC.2016.7804291
    Search in Google ScholarBack to article
  36. [36] I. Ullah, X. Su, X. Zhang, and D. Choi, “Simultaneous localization and mapping based on Kalman filter and extended Kalman filter,” Wireless Communications and Mobile Computing, Art. no. 2138643, Jun. 2020. https://doi.org/10.1155/2020/213864310.1155/2020/2138643
    Search in Google ScholarBack to article
  37. [37] A. Rauf, M. J. Irshad, M. Wasif, S. U. Rasheed, N. Aziz, and H. Taj, “Comparative study of SLAM techniques for UAV,” Engineering Proceedings, vol. 12, no. 1, Art. no.67, Jan. 2022. https://doi.org/10.3390/engproc202101206710.3390/engproc2021012067
    Search in Google ScholarBack to article
  38. [38] M. J. Segura, F. A. Auat Cheein, J. M. Toibero, V. Mut, and R. Carelli, “Ultra wide-band localization and SLAM: A comparative study for mobile robot navigation,” Sensors, vol. 11, no. 2, pp. 2035–2055, Feb. 2011. https://doi.org/10.3390/s11020203510.3390/s110202035327400622319397
    Search in Google ScholarBack to article
  39. [39] U. Ali, W. Muhammad, M.J. Irshad, and S. Manzoor, “Multi-sensor fusion for underwater robot self-localization using PC/BC-DIM neural network,” Sens. Rev., vol. 41, no. 5, Oct. 2021. https://doi.org/10.1108/SR-03-2021-010410.1108/SR-03-2021-0104
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/ecce-2022-0007 | Journal eISSN: 2255-9159
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Language: English
Page range: 50 - 56
Published on: Jan 19, 2023
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
EKF,
extended Kalman filter,
light detecting and range,
linear Kalman filter,
simultaneously localization and mapping,
SLAM,
unmanned aerial vehicle
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2023 Abdul Rauf, Muhammad Jehanzeb Irshad, Muhammad Wasif, Zubair Mehmood, Tayybah Kiren, Nazam Siddique, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.

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