KEYWORDS: Contrast transfer function, LIDAR, Imaging systems, Laser systems engineering, 3D acquisition, Data processing, Stereoscopy, Detection and tracking algorithms, Clouds, 3D image processing
Several quantitative data quality metrics for three dimensional (3D) laser radar systems are presented, namely: X-Y
contrast transfer function, Z noise, Z resolution, X-Y edge & line spread functions, 3D point spread function and data
voids. These metrics are calculated from both raw and/or processed point cloud data, providing different information
regarding the performance of 3D imaging laser radar systems and the perceptual quality attributes of 3D datasets. The
discussion is presented within the context of 3D imaging laser radar systems employing arrays of Geiger-mode
Avalanche Photodiode (GmAPD) detectors, but the metrics may generally be applied to linear mode systems as well. An
example for the role of these metrics in comparison of noise removal algorithms is also provided.
Image chain analysis is a systems engineering tool which allows imaging system designers to understand how different
components of the imaging chain affect the quality and derivable information of generated data products. In this paper,
we apply image chain analysis techniques to formulate a product chain for airborne three-dimensional (3D) imaging
laser radar systems that employ arrays of Geiger-mode avalanche photodiode detectors. The processes involved in 3D
data generation and subsequent information extraction are described. These processes are grouped into five groups (Data
Capture, Raw Point Cloud Formation, Noise Filtering, Advanced Post-Processing and Data Analysis) to form the
proposed product chain. For each group, key parameters that affect 3D data quality are identified along with synthetic
data examples of their respective impact to 3D data quality and information extraction. In addition, we discuss on-going
and future work intended to continue our understanding o3 3D data quality and interpretability.
KEYWORDS: Sensors, LIDAR, Data storage, Pulsed laser operation, Imaging systems, Laser systems engineering, Digital recording, Data modeling, Algorithm development, Data compression
In the past decade arrays of Geiger-mode Avalanche Photodiode (GmAPD) detectors have increased in size from 4×4 to
128×32, resulting in significant increases in data rates. If not handled appropriately, data collected with larger arrays and
higher laser pulse repetition frequencies could potentially stress existing data dissemination and storage infrastructures.
Data compression techniques that reduce storage requirements by taking advantage of data redundancies could be used
to mitigate this problem. In this paper we present an analysis of the coding redundancy that exists in raw data captured
with three dimensional imaging laser radar systems that employ arrays of Geiger-mode Avalanche Photodiode
(GmAPD) detectors. The data we analyzed was collected in three different scanning modes, namely: mapping-mode,
target-mode and stare-mode. We found that there is a significant amount of coding redundancy in raw GmAPD data
which can be used to minimize storage sizes. We demonstrate that a trivial and simple approach reduces data for some
scan patterns. This work represents a first step towards developing robust compression algorithms for raw GmAPD data.
We present considerations for future work.
Conference Committee Involvement (3)
Laser Radar Technology and Applications XIX
6 May 2014 | Baltimore, MD, United States
Laser Radar Technology and Applications XVIII
30 April 2013 | Baltimore, Maryland, United States
Laser Radar Technology and Applications XVII
24 April 2012 | Baltimore, Maryland, United States
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