KEYWORDS: LIDAR, 3D modeling, Data modeling, Software development, Sensors, Visualization, Clouds, Computer simulations, Systems modeling, 3D acquisition
Simulation of LADAR systems is particularly important for the verification of the system design through the
performance assessment. Although many researchers attempted to develop various kinds of LADAR simulators, most of
them have some limitations in being practically used for the general design of diverse types of LADAR system. We thus
attempt to develop high-speed simulation software that is applicable to different types of LADAR system. In summary,
we analyzed the previous studies related to LADAR simulation and, based on those existing works, performed the sensor
modeling in various aspects. For the high-speed operation, we incorporate time-efficient incremental coherent ray-tracing
algorithms, 3D spatial database systems for efficient spatial query, and CUDA based parallel computing. The
simulator is mainly composed of three modules: geometry, radiometry, and visualization modules. Regarding the
experimental results, our simulation software could successfully generate the simulated data based on the pre-defined
system parameters. The validation of simulation results is performed by the comparison with the real LADAR data, and
the intermediate results are promising. We believe that the developed simulator can be widely useful for various fields.
LADAR (Laser Detection and Ranging) is widely used for reconnaissance or target detection by being mounted on
various moving vehicles in the defense field. During the design and development process of a LADAR system, system
simulation is typically performed to assess its performance and to provide test data for the real applications. In order to
generate simulated LADAR data with a high degree of reliability and accuracy, it is required to derive the precise
geometric model of the sensors and to calculate the locations where the rays (laser pulses) reflected using the geometric
model. As ten thousands of laser beams are transmitted to the targets every second during the real operation, a LADAR
simulator should perform a tremendous amount of geometric computations to determine the intersections between the
rays and the targets. In this study, we present an attempt to develop an efficient method for such geometric computation
for LADAR simulation. In the computational process, we first search for the candidate facets which possess a high
possibility to intersect with a ray then determine the actual intersecting facet, and further compute the intersection. To
reduce the computational time, we employ an incremental algorithm and parallel processing based on a CUDA enabled
GPU. We expect that our proposed approaches will enhance LADAR simulator software to be able to run in near realtime.
An airborne LIDAR (LIght Detection And Ranging) system can rapidly generate 3D points by densely sampling the
terrain surfaces using laser pulses. The LIDAR points can be efficiently utilized for automatic reconstruction of 3D
models of the objects on the terrain and the terrain itself. The data simulation of such a LIDAR system is significantly
useful not only to design an optimal sensor for a specific application but also to assess data processing algorithms with
various kinds of test data. In this study, we thus attempted to develop data simulation software of an airborne LIDAR
system generally consisting of a GPS, an IMU and a laser scanner. We focused particularly on the geometric modeling of
the sensors and the object modeling of the targets and background. Hence the data simulation software has been
developed using these models. For the geometric modeling, we derived the sensor equation by modeling not only the
geometric relationships between the three modules, such as a GPS, an IMU and a laser scanner but also the systematic
errors associated with them. Moreover, for rapid and effective simulation, we designed the data model for both targets
and background. We constructed the model data by converting the VRML formatted data into the designed model and
stored these data in a 3D spatial database that can offer more effective 3D spatial indexing and query processing. Finally,
we developed a program that generates simulated data along with the system parameters of a sensor, a terrain model and
its trajectories over the model given.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.