Solution to the SLAM problem in low dynamic environments using a pose graph and an RGB-D sensor

Sensors (Switzerland)

Volumn 14, Issue 7, 2014, Pages 12467-12496

  Lee, Donghwa ^a   Myung, Hyun ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Low dynamic environment; Pose graph; RGB D (red green blue depth); Simultaneous localization and mapping (SLAM)

Indexed keywords

GRAPHIC METHODS; MATHEMATICAL TECHNIQUES; ROBOTICS; SENSORS;

DYNAMIC ENVIRONMENTS; LOCALIZATION AND MAPPINGS; MOBILE ROBOT SYSTEMS; MOVING OBJECT DETECTION ALGORITHMS; POSE GRAPH; RED GREEN BLUES; SIMULTANEOUS LOCALIZATION AND MAPPING; SIMULTANEOUS LOCALIZATION AND MAPPING PROBLEMS;

GRAPH THEORY;

EID: 84904191521     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s140712467     Document Type: Article

References (24)

1. Dissanayake, M.W.M.G.; Newman, P.; Clark, S.; Durrant-Whyte, H.F.; Csorba, M. A Solution to the Simultaneous Localization and Map Building (SLAM) Problem. IEEE Trans. Robot. Autom. 2001, 17, 229-241.
2. Montemerlo, M.; Thrun, S.; Koller, D.; Wegbreit, B. FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico, 9-15 August 2003; pp. 1151-1156.
3. Hahnel, D.; Burgard, W.; Fox, D.; Thrun, S. An Efficient FastSLAM Algorithm for Generating Maps of Large-Scale Cyclic Environments from Raw Laser Range Measurements. In Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 27 October-1 November 2003; pp. 206-211.
4. Grisetti, G.; Stachniss, C.; Grzonka, S.; Burgard, W. A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps Using Gradient Descent. In Proceedings of 2007 Robotics: Science and Systems (RSS), Atlanta, GA, USA, 27-30 June 2007.
5. Kummerle, R.; Grisetti, G.; Strasdat, H.; Konolige, K.; Burgard, W. g2o: A General Framework for Graph Optimization. In Proceedings of 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9-13 May 2011; pp. 3607-3613.
6. Kaess, M.; Ranganathan, A.; Dellaert, F. iSAM: Incremental Smoothing and Mapping. IEEE Trans. Robot. 2008, 24, 1365-1378.
7. Tipaldi, G.D.; Meyer-Delius, D.; Burgard, W. Lifelong Localization in Changing Environments. Int. J. Robot. Res. 2013, 32, 1662-1678.
8. Walcott-Bryant, A.; Kaess, M.; Johannsson, H.; Leonard, J.J. Dynamic Pose Graph SLAM: Long-Term Mapping in Low Dynamic Environments. In Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura-Algarve, Portugal, 7-12 October 2012; pp.1871-1878.
9. Zhao, H.; Chiba, M.; Ryosuke, S.; Shao, X.; Cui, J.; Zha, H. SLAM in a Dynamic Large Outdoor Environment Using a Laser Scanner. In Proceedings of 2008 IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA, 19-23 May 2008; pp. 1455-1462.
10. Vu, T.D.; Olivier, A.; Nils, A. Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments. In Proceedings of 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 13-15 June 2007; pp. 190-195.
11. Kawewong, A.; Tongprasit, N.; Tangruamsub, S.; Hasegawa, O. Online and Incremental Appearance-based SLAM in Highly Dynamic Environments. Int. J. Robot. Res. 2011, 30, 33-55.
12. Zhang, Z. Microsoft Kinect Sensor and Its Effect. IEEE Multimed. 2012, 19, 4-10.
13. Olson, E.; Agarwal, P. Inference on Networks of Mixtures for Robust Robot Mapping. Int. J. Robot. Res. 2013, 32, 826-840.
14. Sunderhauf, N.; Protzel, P. Switchable Constraints for Robust Pose Graph SLAM. In Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura-Algarve, Portugal, 7-12 October 2012; pp. 1879-1884.
15. Agarwal, P.; Tipaldi, G.; Spinello, L.; Stachniss, C.; Burgard, W. Robust map optimization using dynamic covariance scaling. In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 6-10 May 2013; pp. 62-69.
16. Mahalanobis, P.C. On the generalised distance in statistics. Proc. Natl. Inst. Sci. Calcutta 1936, 2 49-55.
17. Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 2004, 60, 91-110.
18. Bay, H.; Ess, A.; Tuytelaars, T.; Gool, L.V. Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 2008, 110, 346359.
19. Fischler, M.A.; Bolles, R.C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM 1981, 24, 381-395.
20. Lee, D.; Kim, H.; Myung, H. GPU-Based Real-Time RGB-D 3D SLAM. In Proceedings of 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Daejeon, Korea, 26-29 November 2012; pp. 46-48.
21. Lee, D.; Kim, H.; Myung, H. 2D Image Feature-Based Real-Time RGB-D 3D SLAM. In Proceedings of Robot Intelligence Technology and Applications (RiTA), Gwangju, Korea, 16-18 December 2012; pp. 485-492.
22. Olson, E.; Leonard, J.; Teller, S. Fast iterative alignment of pose graphs with poor initial estimates. In Proceedings of 2006 IEEE International Conference on Robotics and Automation (ICRA), Orlando, FL, USA, 15-19 May 2006; pp. 2262-2269.
23. Smith, R.C.; Cheeseman, P. On the Representation and Estimation of Spatial Uncertainty. Int. J. Robot. Res. 1986, 5, 56-68.
24. Pioneer 3-AT. Available online: http://www.mobilerobots.com/ResearchRobots/P3AT.aspx (accessed on 25 April 2014).