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Convergent application for trace elimination of dynamic objects from accumulated lidar point clouds

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Abstract

In this paper, a convergent multimedia application for filtering traces of dynamic objects from accumulated point cloud data is presented. First, a fast ground segmentation algorithm is designed by dividing each frame data item into small groups. Each group is a vertical line limited by two points. The first point is orthogonally projected from a sensor’s position to the ground. The second one is a point in the outermost data circle. Two voxel maps are employed to save information on the previous and current frames. The position and occupancy status of each voxel are considered for detecting the voxels containing past data of moving objects. To increase detection accuracy, the trace data are sought in only the nonground group. Typically, verifying the intersection between the line segment and voxel is repeated numerous times, which is time-consuming. To increase the speed, a method is proposed that relies on the three-dimensional Bresenham’s line algorithm. Experiments were conducted, and the results showed the effectiveness of the proposed filtering system. In both static and moving sensors, the system immediately eliminated trace data and maintained other static data, while operating three times faster than the sensor rate.

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Acknowledgements

This work was also supported by a grant from Agency for Defense Development, under contract #UD150017ID.

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Authors and Affiliations

  1. Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul, 04620, Republic of Korea

    Phuong Minh Chu, Seoungjae Cho & Kyungeun Cho

  2. Agency for Defense Development, P.O.Box 35, Yuseong, Daejeon, 34186, Republic of Korea

    Sungdae Sim & Kiho Kwak

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  1. Phuong Minh Chu
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  2. Seoungjae Cho
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  5. Kyungeun Cho
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Correspondence to Kyungeun Cho.

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Chu, P.M., Cho, S., Sim, S. et al. Convergent application for trace elimination of dynamic objects from accumulated lidar point clouds. Multimed Tools Appl 77, 29991–30009 (2018). https://doi.org/10.1007/s11042-017-5089-8

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  • DOI: https://doi.org/10.1007/s11042-017-5089-8

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