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LIDAR Modeling and Application


Research Team: Scott Brown, Daniel Blevins, (Ph.D. student), Michael Foster (Ph.D. student)

Task Scope: RIT has worked with the ITT Industries Space Systems Division (SSD) for the past few years on improving the ability to perform end-to-end, topographic LIDAR system simulations. Most of this effort has been focused on improving the capabilities of the DIRSIG active laser sensing capabilities so that it can integrate better with the sensor and platform modeling capabilities developed by the ITT team. In addition, we have had the cooperation of the MIT Lincoln Laboratories (MIT/LL) which has been sharing specifics of its own internally developed topographic LIDAR system, name ALIRT.

Access to the data from this system has given the RIT and ITT crew valuable insight into the issues associated with modeling real-world topographic LIDAR systems.

Task Status: During the past year, the RIT and ITT team was able to perform analytical verifications and experimental validation of the DIRSIG LIDAR model. Both of these tasks were accomplished in response to the availability of some data collected under controlled conditions with the ALIRT sensor. The MIT/LL team setup the sensor inside an aircraft hanger with a fold mirror under the aircraft to point the sensor and laser across the hanger at a target. Time-gated returns for this target were collected under a variety of power and timing conditions. This data allowed the RIT and ITT team to perform a radiometric verification and validation of the DIRSIG LIDAR model. The numerical verification of the model was performed with an external radiometric calculation that was then compared to the DIRSIG numerical calculation. The validation utilized the hanger data to estimate the radiometric flux onto the actual ALIRT sensor, which was then compared to the numerical calculation produced by the DIRSIG model. The results of the numerical verification and validation were found to be satisfactory.

The second focus over the last year was the incorporation of more instrument and platform noise sources that are present in topographic LIDAR systems. Errors in the platform relative pointing and the platform location and orientation arise from the limited precision of the electro-mechanical devices used to measure these values. Since the precision of these pieces of supporting data has an impact on the ability to geo-locate

LIDAR returns, the effective horizontal and vertical resolution of the overall system is driven in large part by the knowledge of these values. The major update to the DIRSIG model included the ability to incorporate noise or uncertainty into the platform relative pointing of the instrument and into the platform location and orientation. The 3D point clouds in Figure 3.11.3-1 illustrate the impact of errors in platform relative pointing angles (from limited
pointing resolution) on the horizontal resolution of the final data products.

Figure 3.11.3-1: 3D point clouds of a simulated resolution target using noisy platform relative pointing data illustrates the impact of pointing and location knowledge on final data productsFigure 3.11.3-1: 3D point clouds of a simulated resolution target using noisy platform relative pointing data illustrates the impact of pointing and location knowledge on final data productsFigure 3.11.3-1: 3D point clouds of a simulated resolution target using ideal (noise free) pointing and location dataFigure 3.11.3-1: 3D point clouds of a simulated resolution target using ideal (noise free) pointing and location data

Anthony Vodacek
Faculty
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My recent research deals with models of environmental processes and methods for assimilating remote sensing data into those models. Application areas have included water quality and wildland fire monitoring.

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