Abstract
Modeling static and dynamic traffic participants is an important requirement for driving assistance. Reliable speed estimation of obstacles is an essential goal especially when the surrounding environment is crowded and unstructured. In this chapter we propose a solution for real-time motion estimation of obstacles by using the pairwise alignment of object delimiters. Instead of involving the whole 3D point cloud, more compact polygonal models are extracted from a classified digital elevation map and are used as input data for the alignment process.
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References
Franke, U., Rabe, C., Badino, H., Gehrig, S.: 6d-vision: Fusion of stereo and motion for robust environment perception. In: 27th Annual Meeting of the German Association for Pattern Recognition DAGM ’05, pp. 216–223 (2005)
Danescu, R., Nedevschi, S., Meinecke, M.M., Graf, T.: Stereovision based vehicle tracking in urban traffic environments. In: IEEE Intelligent Transportation Systems Conference (ITSC 2007), Seattle, USA (2007)
Pfeiffer, D., Franke., U.: Efficient representation of traffic scenes by means of dynamic stixels. In: IEEE Intelligent Vehicles Symposium (IEEE-IV) 2010, pp. 217–224 (2010)
Wang, C.C., Thorpe. C., Hebert, M., Thrun. S., Durrant-Whyte, H.: Simultaneous localization, mapping and moving object tracking. Int. J. Robot. Res. 26(6), 889–916 (2007)
Prakash, S., Thomas, S.: Contour tracking with condensation/stochastic search. In: Department of CSE. IIT Kanpur (2007)
Yokoyama, M., Poggio, T.: A contour-based moving object detection and tracking. In: 14th International Conference on Computer Communications and Networks (ICCCN ’05), pp. 271–276, IEEE Computer Society Washington, DC, USA (2005)
Thomas de Candia: 3d tracking of dynamic objects with a laser and a camera. Technical report, Autonomous System Lab, ETH Zurich (2010)
Madhavan, R.: Terrain aided localization of autonomous vehicles. In: Symposium on Automation and Robotics in Construction, Gaithersburg (2002)
Fairfield, N., Kantor, G.A., Wettergreen, D.: Real-time SLAM with octree evidence grids for exploration in underwater tunnels. J. Field Robot. 24, 3–21 (2007)
Danescu, R., Oniga, F., Nedevschi, S.: Modeling and tracking the driving environment with a particle based occupancy grid. IEEE Trans. Intell. Transp. Syst. 12(4), 1331–1342 (2012)
Hess, J.M.: Characterizing dynamic objects in 3d laser range data. Master’s thesis, Albert-Ludwigs-Universitt Freiburg (2008)
Danescu, R, Oniga, F., Nedevschi, S., Meinecke, M.-M.: Tracking multiple objects using particle filters and digital elevation maps. In: IEEE Intelligent Vehicles Symposium (IEEE-IV 2009), pp. 88–93, Xi’An, China (2009)
Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Besl P., McKay, N.: A method for registration of 3d shape. Trans. Pattern Anal Mach. Intell. 12(2), 239–256 (1992)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Third International Conference on 3D Digital Imaging and Modeling (2001)
Vatavu, A., Nedevschi, S., Oniga, F.: Real time object delimiters extraction for environment representation in driving scenarios. In: ICINCO-RA 2009, pp. 86–93, Milano, Italy (2009)
Nedevschi, S., Danescu, R., Marita, T., Oniga, F., Pocol, C., Sobol, S., Tomiuc, C., Vancea, C., Meinecke, M.M., Graf, T., To, T.B., Obojski, M.A.: A sensor for urban driving assistance systems based on dense stereovision. In: Intelligent Vehicles 2007, pp. 278–286, Istanbul (2007)
Woodill, J.I., Gordon, G., Buck, R.: Tyzx deepsea high speed stereo vision system. In: IEEE Computer Society Workshop on Real Time 3-D Sensors and Their Use, Conference on Computer Vision and, Pattern Recognition (2004)
Oniga, F., Nedevschi, S.: Processing dense stereo data using elevation maps: road surface, traffic isle, and obstacle detection. IEEE Trans. Veh. Technol. 59(3), 1172–1182 (2010)
Douglas, D., Peuker, T.: Algorithms for the reduction of the number of points required to represent a digitised line or its caricature. Can. Cartographer 10, 112–122 (1973)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Borgefors, G.: Distance transformations in arbitrary dimensions. Comput. Vis. Graph. Image Proc. 27, 321–345 (1984)
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Vatavu, A., Nedevschi, S. (2014). Vision Based Motion Estimation of Obstacles in Dynamic Unstructured Environments. In: Ferrier, JL., Bernard, A., Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-319-03500-0_15
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DOI: https://doi.org/10.1007/978-3-319-03500-0_15
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