Error Estimations in the Design of a Terrain Measurement System
Abstract
Terrain surface measurement is an important tool in vehicle design work as well as pavement classification and health monitoring. �Non-deformable terrains are the primary excitation to vehicles traveling over it, and therefore it is important to be able to quantify these terrain surfaces. Knowledge of the terrain can be used in combination with vehicle models in order to predict force loads the vehicles would experience while driving over the terrain surface. �This is useful in vehicle design, as it can speed the design process through the use of simulation as opposed to prototype construction and durability testing. �Additionally, accurate terrain maps can be used by highway engineers and maintenance personnel to identify deterioration in road surface conditions for immediate correction. �Repeated measurements of terrain surfaces over an extended length of time can also allow for long term pavement health monitoring.
Many systems have been designed to measure terrain surfaces, most of them historically single line profiles, with more modern equipment capable of capturing three dimensional measurements of the terrain surface. �These more modern systems are often constructed using a combination of various sensors which allow the system to measure the relative height of the terrain with respect to the terrain measurement system. �Additionally, these terrain measurement systems are also equipped with sensors which allow the system to be located in some global coordinate space, as well as the angular attitude of that system to be estimated. �Since all sensors return estimated values, with some uncertainty, the combination of a group of sensors serves to also combine their uncertainties, resulting in a system which is less precise than any of its individual components. �In order to predict the precision of the system, the individual probability densities of the components must be quantified, in some cases transformed, and finally combined in order to predict the system precision. �This thesis provides a proof-of-concept as to how such an evaluation of final precision can be performed.
Many systems have been designed to measure terrain surfaces, most of them historically single line profiles, with more modern equipment capable of capturing three dimensional measurements of the terrain surface. �These more modern systems are often constructed using a combination of various sensors which allow the system to measure the relative height of the terrain with respect to the terrain measurement system. �Additionally, these terrain measurement systems are also equipped with sensors which allow the system to be located in some global coordinate space, as well as the angular attitude of that system to be estimated. �Since all sensors return estimated values, with some uncertainty, the combination of a group of sensors serves to also combine their uncertainties, resulting in a system which is less precise than any of its individual components. �In order to predict the precision of the system, the individual probability densities of the components must be quantified, in some cases transformed, and finally combined in order to predict the system precision. �This thesis provides a proof-of-concept as to how such an evaluation of final precision can be performed.
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