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Automatic change detection using mobile laser scanning

: Hebel, Marcus; Hammer, Marcus; Gordon, Marvin; Arens, Michael

Volltext urn:nbn:de:0011-n-3106370 (3.3 MByte PDF)
MD5 Fingerprint: 6340500353b097d964df8c1c85f7d02f
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 28.10.2014

Bishop, G. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II : 13.10.2014, Amsterdam
Bellingham, WA: SPIE, 2014 (Proceedings of SPIE 9250)
ISBN: 978-1-62841-313-7
Paper 92500I, 10 S.
Conference "Electro-Optical Remote Sensing, Photonic Technologies, and Applications" <8, 2014, Amsterdam>
Conference "Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing" <2, 2014, Amsterdam>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
laser scanning; LiDAR; 3D; MLS; ALS; change detection; multi-aspect; multi-view; multi-temporal

Automatic change detection in 3D environments requires the comparison of multi-temporal data. By comparing current data with past data of the same area, changes can be automatically detected and identified. Volumetric changes in the scene hint at suspicious activities like the movement of military vehicles, the application of camouflage nets, or the placement of IEDs, etc. In contrast to broad research activities in remote sensing with optical cameras, this paper addresses the topic using 3D data acquired by mobile laser scanning (MLS). We present a framework for immediate comparison of current MLS data to given 3D reference data. Our method extends the concept of occupancy grids known from robot mapping, which incorporates the sensor positions in the processing of the 3D point clouds. This allows extracting the information that is included in the data acquisition geometry. For each single range measurement, it becomes apparent that an object reflects laser pulses in the measured range distance, i.e., space is occupied at that 3D position. In addition, it is obvious that space is empty along the line of sight between sensor and the reflecting object. Everywhere else, the occupancy of space remains unknown. This approach handles occlusions and changes implicitly, such that the latter are identifiable by conflicts of empty space and occupied space. The presented concept of change detection has been successfully validated in experiments with recorded MLS data streams. Results are shown for test sites at which MLS data were acquired at different time intervals.