This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment

Journal Article
2021-01-0250
ISSN: 2641-9645, e-ISSN: 2641-9645
Published April 06, 2021 by SAE International in United States
Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment
Sector:
Citation: Kurup, A., Kysar, S., Bos, J., Jayakumar, P. et al., "Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(5):2337-2344, 2021, https://doi.org/10.4271/2021-01-0250.
Language: English

References

  1. Bellutta , P. , Manduchi , R. , Matthies , L. , Owens , K. , and Rankin , A. Terrain Perception for Demo III Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511) 326 331 Oct. 2000
  2. Rankin , A.L. and Matthies , L.H. 2008
  3. Laible , S. , Khan , Y.N. , Bohlmann , K. , and Zell , A. 2012
  4. Sadhukhan , D. and Moore , C.A. On-Line Terrain Estimation Using Internal Sensors Florida Conf. on Recent Advances in Robotics 195 199 Citeseer 2003
  5. Pomerleau , D.A. Alvinn: An Autonomous Land Vehicle in a Neural Network Advances in Neural Information Processing Systems 305 313 1989
  6. Kurup , A. , Kysar , S. , and Bos , J. SVM-Based Sensor Fusion for Improved Terrain Classification Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020 11415 121 128 International Society for Optics and Photonics, SPIE 2020
  7. Manduchi , R. , Castano , A. , Talukder , A. , and Matthies , L. Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation Autonomous Robots 18 81 102 2005
  8. Angelova , A. , Matthies , L. , Helmick , D. , and Perona , P. Learning and Prediction of Slip from Visual Information Journal of Field Robotics 24 3 205 231 2007
  9. Weiss , C. , Fröhlich , H. , and Zell , A. Vibration-Based Terrain Classification using Support Vector Machines 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems 4429 4434 2006
  10. Ojeda , L. , Borenstein , J. , Witus , G. , and Karlsen , R.E. Terrain Characterization and Classification with a Mobile Robot J. Field Robotics 23 103 122 2006
  11. DuPont , E.M. , Moore , C.A. , Collins , E.G. , and Coyle , E. Frequency Response Method for Terrain Classification in Autonomous Ground Vehicles Autonomous Robots 24 337 347 May 2008
  12. Brooks , C. and Iagnemma , K. Vibration-Based Terrain Classification for Planetary Exploration Rovers IEEE Transactions on Robotics 21 6 1185 1191 2005
  13. DuPont , E.M. , Roberts , R.G. , and Moore , C.A. The Identification of Terrains for Mobile Robots using Eigenspace and Neural Network Methods Proc. of the Florida Conf. on Recent Advances in Robotics Citeseer 2006
  14. Christian , W. , Nikolas , F. , Matthias , S. , and Andreas , Z. Comparison of Different Approaches to Vibration-Based Terrain Classification Proceedings of the European Conference on Mobile Robotics 2007
  15. Happold , M. , Ollis , M. , and Johnson , N. Enhancing Supervised Terrain Classification with Predictive Unsupervised Learning Robotics: Science and Systems Citeseer 2006
  16. Zürn , J. , Burgard , W. , and Valada , A. Dec. 2019
  17. Brooks , C.A. and Iagnemma , K. Self-Supervised Terrain Classification for Planetary Surface Exploration Rovers J. Field Robot. 29 445 468 May 2012
  18. Kahn , G. , Abbeel , P. , and Levine , S. 2020
  19. Wellhausen , L. , Dosovitskiy , A. , Ranftl , R. , Walas , K. et al. Where Should I Walk? Predicting Terrain Properties from Images Via Self-Supervised Learning IEEE Robotics and Automation Letters 4 2 1509 1516 2019
  20. Quigley , M. , Conley , K. , Gerkey , B.P. , Faust , J. , Foote , T. , Leibs , J. , Wheeler , R. , and Ng , A.Y. Ros: An Open-Source Robot Operating System ICRA Workshop on Open Source Software 2009
  21. Hoffmann , H. Kernel PCA for Novelty Detection Pattern Recognition 40 3 863 874 2007
  22. Welch , P. The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms IEEE Transactions on Audio and Electroacoustics 15 2 70 73 1967
  23. Pedregosa , F. , Varoquaux , G. , Gramfort , A. , Michel , V. et al. Scikit-Learn: Machine Learning in Python Journal of Machine Learning Research 12 2825 2830 2011
  24. Kuo , B.-C. , Ho , H.-H. , Li , C.-H. , Hung , C.-C. , and Taur , J.-S. A Kernel-Based Feature Selection Method for SVM with RBF Kernel for Hyperspectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 1 317 326 2013
  25. Arthur , D. and Vassilvitskii , S. K-Means++: The Advantages of Careful Seeding Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms SODA ’07 USA 1027 1035 Society for Industrial and Applied Mathematics 2007
  26. Sculley , D. Web-Scale K-Means Clustering Proceedings of the 19th International Conference on World Wide Web, WWW ’10 New York, NY, USA 1177 1178 Association for Computing Machinery 2010
  27. Strehl , A. and Ghosh , J. Cluster Ensembles—A Knowledge Reuse Framework for Combining Multiple Partitions J. Mach. Learn. Res. 3 583 617 Mar. 2003

Cited By