DocumentCode :
3568444
Title :
Compressive sampling of LIDAR: Full-waveforms as signals of finite rate of innovation
Author :
Castorena, Juan ; Creusere, Charles D.
Author_Institution :
Klipsch Sch. of Electr. & Comput. Eng., New Mexico State Univ., Las Cruces, NM, USA
fYear :
2012
Firstpage :
984
Lastpage :
988
Abstract :
The 3D imaging community has begun a transition to full-waveform (FW) LIDAR systems which image a scene by emitting laser pulses in a particular direction and capturing the entire temporal envelope of each echo. By scanning a region, connected 1D profile waveforms of the 3D scenes can be readily obtained. In general, FW systems capture more detailed physical information and characteristic properties of the 3D scenes versus conventional 1st and 2nd generation LIDARs which simply store clouds of range points. Unfortunately, the collected datasets are very large, making tasks like processing, storage, and transmission far more resource-intensive. Current compression approaches addressing these issues rely on collecting large amounts of data and then analyzing it to identify perceptual and statistical redundancies which are subsequently removed. Collecting large amounts of data just to discard most of it is highly inefficiently. Our approach to LIDAR compression models FW return pulses as signals with finite rate of innovation (FRI). We show in this paper that sampling can be performed at the rate of innovation while still achieving good quality reconstruction. Specifically, we show that efficient sampling and compression can be achieved on actual LIDAR FW´s within the FRI framework.
Keywords :
data compression; image coding; optical radar; radar imaging; statistical analysis; 1D profile waveforms; 3D imaging community; 3D scenes; FRI framework; FW LIDAR systems; FW return pulses; LIDAR compression models; LIDAR compressive sampling; finite rate of innovation; finite rate signals; full-waveform LIDAR systems; perceptual redundancy identification; physical information; statistical redundancy identification; Approximation methods; Discrete Fourier transforms; Laser radar; Shape; Signal resolution; Splines (mathematics); Technological innovation; LIDAR; compressive sampling; finite rate of innovation; full-waveform; sparsity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
ISSN :
2219-5491
Print_ISBN :
978-1-4673-1068-0
Type :
conf
Filename :
6333916
Link To Document :
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