One of the most important problems in real-time, mobile augmented reality is *registration error* -- the misalignment between the computer generated graphics and the physical world the application is trying to augment. Such misalignment may either cause the information presented by the application to be misleading to the user or make the augmentation meaningless. In this work, we question the implied assumption that registration error must be eliminated for AR to be useful. Instead, we take the position that registration error will never be eliminated and that application developers can build useful AR applications if they have an estimate of registration error. We present a novel approach to AR application design: *Spatially Adaptive Augmented Reality* (i.e., applications that change their displays based on the quality of the alignment between the physical and virtual world). The computations used to change the display are based on real-time estimates of the registration error. The application developer uses these estimates to build applications that function under a variety of conditions independent of specific tracking technologies. In addition to introducing Spatially Adaptive AR, this research establishes a theoretical model for AR. These theoretical contributions are manifested in a toolkit that supports the design of Spatially Adaptive AR applications: OSGAR. This work describes OSGAR in detail and presents examples that demonstrate how to use this novel approach to create adaptable augmentations as well as how to support user interaction in the presence of uncertainty.

Background: The objective of this study was to verify the effect that plot locational errors, termed plot co-registration errors, have on the strength of regression between LiDAR canopy metrics and the measured total standing volume (TSV) of plots in a Pinus radiata forest. Methods: A 737 hectare plantation of mature Pinus radiata located in Northern Hawkes Bay was selected for the study. This forest had been measured in a pre-harvest inventory and had aerial LiDAR assessment. The location of plots was verified using a survey-grade GPS. Least square linear regression models were developed to predict TSV from LiDAR canopy metrics for a sample of 204 plots. The regression strength, accuracy and bias was compared for models developed using either the actual (verified) or the incorrect (intended) locations for these plots. The change to the LiDAR canopy metrics after the plot co-registration errors was also established. Results: The plot co-registration error in the sample ranged from 0.7 m to 70.3 m, with an average linear spatial error of 10.6 m. The plot co-registration errors substantially reduced the strength of regression between LiDAR canopy metrics and TSV, as the model developed from the actual plot locations had an R2 of 44%, while the model developed from the incorrect plot locations had an R2 of 19%. The greatest reductions in model strength occurred when there was less than a 60% overlap between the plots defined by correct and incorrect locations. Higher plot co-registration errors also caused significant changes to the height and density LiDAR canopy metrics that were used in the regression models. The lower percentile elevation LiDAR metrics were more sensitive to plot co- registration errors, compared to higher percentile metrics. Conclusion: Plot co-registration errors have a significant effect on the strength of regressions formed between TSV and LiDAR canopy metrics. This indicates that accurate measurements of plot locations are necessary to fully utilise LiDAR for inventory purposes in forests of Pinus radiata.

abstract: Multi-pulse particle tracking velocimetry (multi-pulse PTV) is a recently proposed flow measurement technique aiming to improve the performance of conventional PTV/ PIV. In this work, multi-pulse PTV is assessed based on PTV simulations in terms of spatial resolution, velocity measurement accuracy and the capability of acceleration measurement. The errors of locating particles, velocity measurement and acceleration measurement are analytically calculated and compared among quadruple-pulse, triple-pulse and dual-pulse PTV. The optimizations of triple-pulse and quadruple-pulse PTV are discussed, and criteria are developed to minimize the combined error in position, velocity and acceleration. Experimentally, the velocity and acceleration fields of a round impinging air jet are measured to test the triple-pulse technique. A high speed beam-splitting camera and a custom 8-pulsed laser system are utilized to achieve good timing flexibility and temporal resolution. A new method to correct the registration error between CCDs is also presented. Consequently, the velocity field shows good consistency between triple-pulse and dual-pulse measurements. The mean acceleration profile along the centerline of the jet is used as the ground truth for the verification of the triple-pulse PIV measurements of the acceleration fields. The instantaneous acceleration field of the jet is directly measured by triple-pulse PIV and presented. Accelerations up to 1,000 g's are measured in these experiments. / Dissertation/Thesis / M.S. Mechanical Engineering 2014