Abstract
In this chapter we introduce cooperating techniques for environment perception and reconstruction based on dynamic point cloud sequences of a single rotating multi-beam (RMB) Lidar sensor, which monitors the scene either from a moving vehicle top or from a static installed position. The joint aim of the addressed methods is to create 4D spatio-temporal models of large dynamic urban scenes containing various moving and static objects. Standalone RMB Lidar devices have been frequently applied in robot navigation tasks and proved to be efficient in moving object detection and recognition. However, they have not been widely exploited yet in video surveillance or dynamic virtual city modeling. We address here three different application areas of RMB Lidar measurements, starting from people activity analysis, through real time object perception for autonomous driving, until dynamic scene interpretation and visualization. First we introduce a multiple pedestrian tracking system with short term and long term person assignment steps. Second we present a model based real-time vehicle recognition approach. Third we propose techniques for geometric approximation of ground surfaces and building facades using the observed point cloud streams. This approach extracts simultaneously the reconstructed surfaces, motion information and objects from the registered dense point cloud completed with point time stamp information.
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Acknowledgments
This work is partially connected to the i4D project funded by the internal R&D grant of MTA SZTAKI, and it was partially supported by the Government of Hungary through a European Space Agency (ESA) Contract under the Plan for European Cooperating States (PECS), and by the Hungarian Research Fund (OTKA #101598).
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Börcs, A., Nagy, B., Benedek, C. (2015). Dynamic 3D Environment Perception and Reconstruction Using a Mobile Rotating Multi-beam Lidar Scanner. In: Busoniu, L., Tamás, L. (eds) Handling Uncertainty and Networked Structure in Robot Control. Studies in Systems, Decision and Control, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-26327-4_7
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DOI: https://doi.org/10.1007/978-3-319-26327-4_7
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