Fusion of laser and monocular camera data in object grid maps for vehicle environment perception

FUSION 2014 - 17th International Conference on Information Fusion

Volumn , Issue , 2014, Pages

  Nuss, Dominik ^a   Thom, Markus ^a   Danzer, Andreas ^a   Dietmayer, Klaus ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

RANGE FINDING;

AUTONOMOUS DRIVING; DEMPSTER-SHAFER THEORY OF EVIDENCE; ENVIRONMENT PERCEPTIONS; ENVIRONMENTAL MODEL; MODULAR FRAMEWORK; OBJECT INFORMATION; OCCUPANCY GRID MAP; PROCESS CLASSIFICATION;

CAMERAS;

EID: 84910681761     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. C. Laugier, I. Paromtchik, M. Perrollaz, M. Yong, J. Yoder, C. Tay, K. Mekhnacha, and A. Negre, "Probabilistic analysis of dynamic scenes and collision risks assessment to improve driving safety, " IEEE Intelligent Transportation Systems Magazine, vol. 3, no. 4, pp. 4-19, 2011.
2. C. Urmson et al., "Autonomous driving in urban environments: Boss and the urban challenge, " Journal of Field Robotics, Special Issue on the 2007 DARPA Urban Challenge, Part I, vol. 25, no. 8, pp. 425-466, 2008.
3. A. Petrovskaya, M. Perrollaz, L. Oliveira, L. Spinello, R. Triebel, A. Makris, J.-D. Yoder, C. Laugier, U. Nunes, and P. Bessiere, "Awareness of road scene participants for autonomous driving, " in Handbook of Intelligent Vehicles, A. Eskandarian, Ed. Springer, 2012, pp. 1383-1432.
4. A. Elfes, "Using occupancy grids for mobile robot perception and navigation, " Computer, vol. 22, no. 6, pp. 46-57, 1989.
5. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. The MIT Press, 2005.
6. R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. Cambridge University Press, 2004.
7. S. Tuohy, D. O'Cualain, E. Jones, and M. Glavin, "Distance determination for an automobile environment using inverse perspective mapping in OpenCV, " in IET Irish Signals and Systems Conference, 2010, pp. 100-105.
8. M. Konrad, D. Nuss, and K. Dietmayer, "Localization in digital maps for road course estimation using grid maps, " in IEEE Intelligent Vehicles Symposium, 2012, pp. 87-92.
9. H. Lategahn, W. Derendarz, T. Graf, B. Kitt, and J. Effertz, "Occupancy grid computation from dense stereo and sparse structure and motion points for automotive applications, " in IEEE Intelligent Vehicles Symposium, 2010, pp. 819-824.
10. H. Badino, U. Franke, and R. Mester, "Free space computation using stochastic occupancy grids and dynamic, " in International Conference on Computer Vision, Workshop on Dynamical Vision, 2007.
11. M. Bertozzi and A. Broggi, "Real-time lane and obstacle detection on the GOLD system, " in IEEE Intelligent Vehicles Symposium, 1996, pp. 213-218.
12. H. Badino, U. Franke, and D. Pfeiffer, "The stixel world-A compact medium level representation of the 3D-world, " in Proceedings of the DAGM Symposium on Pattern Recognition. Springer-Verlag, 2009, pp. 51-60.
13. B. Dasarathy, "Sensor fusion potential exploitation-innovative architectures and illustrative applications, " Proceedings of the IEEE, vol. 85, no. 1, pp. 24-38, 1997.
14. C. Farabet, C. Couprie, L. Najman, and Y. LeCun, "Learning hierarchical features for scene labeling, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1915-1929, 2013.
15. D. Klimentjew, N. Hendrich, and J. Zhang, "Multi sensor fusion of camera and 3D laser range finder for object recognition, " in IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2010, pp. 236-241.
16. G. Shafer, A Mathematical Theory of Evidence. Princeton University Press, 1976.
17. R. Yager, M. Fedrizzi, and J. Kacprzyk, Eds., Advances in the Dempster-Shafer Theory of Evidence. Wiley, 1994.
18. P. Smets, "The combination of evidence in the transferable belief model, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 5, pp. 447-458, 1990.
19. Z. Yi, Y. K. Ho, C. S. Chua, and X. W. Zhou, "Multi-ultrasonic sensor fusion for autonomous mobile robots, " in SPIE Conference Volume 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, 2000, pp. 314-321.
20. C.-C. Wang, C. Thorpe, S. Thrun, M. Hebert, and H. Durrant-Whyte, "Simultaneous localization, mapping and moving object tracking, " International Journal of Robotics Research, vol. 26, no. 9, pp. 889-916, 2007.
21. D. Nuss, B. Wilking, J. Wiest, H. Deusch, S. Reuter, and K. Dietmayer, "Decision-free true positive estimation with grid maps for multi-object tracking, " in International IEEE Conference on Intelligent Transportation Systems, 2013, pp. 28-34.
22. C. M. Bishop, Neural Networks for Pattern Recognition. Clarendon Press, 1995.
23. E. Wan and R. van der Merwe, "The unscented Kalman filter for nonlinear estimation, " in IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium, 2000, pp. 153-158.
24. L. A. Zadeh, On the validity of Dempster's rule of combination of evidence. Electronics Research Laboratory, University of California, 1979.
25. J. Dezert, P. Wang, and A. Tchamova, "On the validity of Dempster-Shafer theory, " in International Conference on Information Fusion, 2012, pp. 655-660.
26. J. Schubert, "Conflict management in Dempster-Shafer theory by sequential discounting using the degree of falsity, " in International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, vol. 8, 2008.
27. F. Campos and S. Cavalcante, "An extended approach for Dempster-Shafer theory, " in IEEE International Conference on Information Reuse and Integration, 2003, pp. 338-344.
28. R. Haenni, "Shedding new light on Zadeh's criticism of Dempster's rule of combination, " in International Conference on Information Fusion, 2005, pp. 879-884.
29. R. Mahler, "Can the Bayesian and Dempster-Shafer approaches be reconciled? Yes." in International Conference on Information Fusion, 2005, pp. 864-871.
30. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001, pp. I-511-I-518.