Future Work
While much progress has been made on developing the
ULEM
concept, there is more work to be done. One area of future
work is in obtaining better covariance and correlation esti-
mates of the parameters used to populate the model. In addi-
tion, more work is needed on the storage and dissemination
of post-adjustment cross-covariance data between files.
ULEM
does not currently provide a method to store the cross-cova-
riance results
between multiple files
used in a multi-file ad-
justment, even though the updated per-file full-covariance is
stored. Implementing
ULEM
into concepts such as the Geopo-
sitioning Metadata Model (
GMM
) currently under development
by
NGA
would provide for the storage of this vital metadata.
Summary
The concept of the Universal Lidar Error Model (
ULEM
) was
described.
ULEM
is an error model that approaches the rigor
and utility of the detailed physical sensor model while allow-
ing for standardization of metadata and ease of storage, and
providing a common mechanism for the error propagation
and data adjustment of lidar datasets. Through the
ULEM
Spec-
ification, the sensor-specific metadata aspects are eliminated
by mapping these metadata to a few standardized parameters.
This
ULEM
metadata is stored in existing file formats (such as
LAS
) that are used by the community, and maintains compli-
ance with those format specifications. Additionally, the ex-
ploitation methods are standardized based on existing sensor
modeling efforts of the community. While the literature has
shown the benefits of the sensor modeling and error propaga-
tion,
ULEM
makes those concepts accessible to the lidar users.
ULEM
employs two implementation modes, Sensor-Space
ULEM
and Ground-Space
ULEM
. Sensor-Space
ULEM
offers an
effectual generic physical sensor model, consolidating the
parameters from the full physical model into a few standard-
ized parameters. In certain cases where this storage of sensor
parameters becomes impractical and/or unwieldy,
ULEM
offers
a ground-space implementation. Ground-Space
ULEM
repre-
sents an efficient strategy for the storage and exploitation of
error covariances in point space. Both implementations pro-
vide methods to model the correlations between adjustable
parameters and provide the ability to generate full covariance
matrices among multiple points. For both implementations,
a specification exists (
NGA
, 2013) to store the
ULEM
metadata
in an
LAS
point cloud file, utilizing the
LAS
variable length
records construct with minimal impact to the file size.
An implementation of Sensor-Space
ULEM
was demon-
strated, including the
ULEM
generation and exploitation, and
an analysis of the
ULEM
predicted uncertainties when com-
pared to ground control. The
ULEM
predicted uncertainties
compared very well to errors calculated using the surveyed
control, demonstrating the error propagation utility of
ULEM
.
Acknowledgements
The authors thank Woolpert, Incorporated, specifically Qas-
sim Abdullah, Bob Brinkman, Layton Hobbs, and Qian Xiao,
for providing the lidar dataset and metadata used for the
ULEM
implementation in this study. Their availability, knowledge,
and expertise were invaluable in this effort.
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