ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 159-165, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
02 Jun 2016
C. Toth1, G. Jozkow1,2, Z. Koppanyi1,3, S. Young1, and D. Grejner-Brzezinska1 1Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Ave., Columbus, OH 43210, USA
2Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wroclaw, Poland
3Department of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 3 Műegyetem rkp., Budapest, 1111, Hungary
Keywords: LiDAR, object detection, pose estimation, motion estimation, airport safety Abstract. Improving sensor performance, combined with better affordability, provides better object space observability, resulting in new applications. Remote sensing systems are primarily concerned with acquiring data of the static components of our environment, such as the topographic surface of the earth, transportation infrastructure, city models, etc. Observing the dynamic component of the object space is still rather rare in the geospatial application field; vehicle extraction and traffic flow monitoring are a few examples of using remote sensing to detect and model moving objects. Deploying a network of inexpensive LiDAR sensors along taxiways and runways can provide both geometrically and temporally rich geospatial data that aircraft body can be extracted from the point cloud, and then, based on consecutive point clouds motion parameters can be estimated. Acquiring accurate aircraft trajectory data is essential to improve aviation safety at airports. This paper reports about the initial experiences obtained by using a network of four Velodyne VLP- 16 sensors to acquire data along a runway segment.
Conference paper (PDF, 1022 KB)

Citation: Toth, C., Jozkow, G., Koppanyi, Z., Young, S., and Grejner-Brzezinska, D.: MONITORING AIRCRAFT MOTION AT AIRPORTS BY LIDAR, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 159-165,, 2016.

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