Browsing by Author "Chan, Ting On"
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- ItemOpen AccessCylindrical and Polygonal Object Modelling and its use in LiDAR Calibration and Point Cloud Registration(2015-01-30) Chan, Ting On; Lichti, DerekLiDAR systems are optical metrological instruments that capture surfaces of objects as highly redundant sets of discrete points (known as point clouds) in a 3D coordinate system from which spatial information can be extracted to support many applications. The accuracy of the LiDAR measurements can be improved by performing appropriate calibration. In this thesis, two novel cylinder-based calibration methods are presented for recovering interior systematic errors of different types of the LiDAR systems. The first method is a cylinder-based self-calibration technique which primarily uses point clouds of vertical cylindrical features captured from several static scan locations. The method is suitable for LiDAR systems with time-invariant errors. The second calibration method is for multi-beam spinning LiDAR systems. It allows frequent, repeated calibrations to be performed in either static or kinematic scanning mode for recovering the time-varying interior errors. For the calibration in kinematic mode, roadside power and lamp poles are used as the calibration references. In addition to these, a new geometric model of octagonal lamp poles along with a new model-driven point cloud registration method is proposed. The new geometric model uses the rotational symmetry property of the polygon to overcome the challenges of modelling the polygonal periphery using a single equation instead of piecewise functions. The proposed registration method is based on the model and requires only point clouds of a single octagonal lamp pole as registration primitives, and no actual overlap between the point clouds of the pole captured at different scan locations is needed. Each proposed method was individually verified with several real datasets, and most of them were also tested with some simulated datasets. The results suggest that all the proposed methods are practical and also offer improvements compared to the existing methods. The main contribution of this work is that many cylindrical and polygonal objects already exist in the built environment can now be used for sensor calibration, point cloud registration, and some other potential new applications as discussed in the last chapter of this thesis.
- ItemOpen AccessFeature-based boresight self-calibration of a mobile mapping system(2011) Chan, Ting On; Lichti, Derek
- ItemOpen AccessLinear regression with an observation distribution model(Springer, 2021-01-18) Lichti, Derek D; Chan, Ting On; Belton, DavidDespite the high complexity of the real world, linear regression still plays an important role in estimating parameters to model a physical relationship between at least two variables. The precision of the estimated parameters, which can usually be considered as an indicator of the solution quality, is conventionally obtained from the inverse of the normal equations matrix for which intensive computation is required when the number of observations is large. In addition, the impacts of the distribution of the observations on parameter precision are rarely reported in the literature. In this paper, we propose a new methodology to model the distribution of observations for linear regression in order to predict the parameter precision prior to actual data collection and performing the regression. The precision analysis can be readily performed given a hypothesized data distribution. The methodology has been verified with several simulated and real datasets. The results show that the empirical and model-predicted precisions match very well, with discrepancies of up to 6% and 3.4% for simulated and real datasets, respectively. Simulations demonstrate that these differences are simply due to finite sample size. In addition, simulation also demonstrates the relative insensitivity of the method to noise in the independent regression variables that causes deviations from the data distribution function. The proposed methodology allows straightforward prediction of the parameter precision based on the distribution of the observations related to their numerical limits and geometry, which greatly simplify design procedures for various experimental setups commonly involved in geodetic surveying such as LiDAR data collection.