Robust shape from depth images with GR2T

This paper proposes to infer accurately a 3D shape of an object captured by a depth camera from multiple view points. The Generalised Relaxed Radon Transform (GR2T) [1] is used here to merge all depth images in a robust kernel density estimate that models the surface of an object in the 3D space. The kernel is tailored to capture the uncertainty associated with each pixel in the depth images. The resulting cost function is suitable for stochastic exploration with gradient ascent algorithms when the noise of the observations is modelled with a differentiable distribution. When merging several depth images captured from several view points, extrinsic camera parameters need to be known accurately, and we extend GR2T to also estimate these nuisance parameters. We illustrate qualitatively the performance of our modelling and we assess quantitatively the accuracy of our 3D shape reconstructions computed from depth images captured with a Kinect camera

Error Metric for Indoor 3D Point Cloud Registration

An increase in commercial availability of 3D scanning technology has led to an increase of 3D perception for a variety of applications. High quality scanners require to be stationary and so multiple scans are required and subsequently need to be registered. A new error metric for registration based on the deviation of registered planar surfaces is introduced here and compared with a commonly used metric: mean square point-to-point distance. Four different sets of features are used to register six scans, the point-to-point errors are compared to the new error metric, planar surface deviation, and a disparity is observed for certain sets of features. The two metrics agree as to which sets of features gave the best registration but disagree as to which set produced the worst registration. It is concluded that further analysis and evaluation is required to determine which metric is more meaningful as a representative measure of registration accuracy and to also investigate other error metrics

Dynamic Model of a Two-Stage Speed Reducer Gearbox for ACC Fans.

Mechanical and Mechatronic Engineerin

The design of a relational database on the geotechnical properties of Northern England glacial till

PhD ThesisThe landscape of Northern England has been mostly formed by glacial activities during the Quaternary period, and glacial till materials have been deposited over the northern counties of England during these glacial activities. Townships, industrial developments and infrastructure works exist or are planned in these areas. The variable and often complex successions in which glacial tills occur have frequently led to problems on civil and mining engin eering projects. Glacial tills are engineering soils which have been defined as a poorly sorted mixture of clay, silt, sand, gravel, cobble and boulder sized material deposited directly from glacier ice. The glacial tills of the counties in Northern England are the subject of many studies which are carried out in order to determine the properties of the overlying glacial deposits. Ground investigations have been carried out for opencast coal projects. A large number of samples were obtained and extensive laboratory testing has been carried out. Using the results of these investigations and tests, a geotechnical database is being developed that should provide a useful resource for civil and mining engineers in the northern counties region. Its purpose is the extensive analysis of the parameters that are used to define the geotechnical properties of Northern England glacial tills. This should give a better understanding of the engineering behaviour of glacial tills and parameter selection for engineering design. In addition to statistical analysis, Neural Networks, a model of Artificial Intelligence, are used to find correlations between the different parameters and to develop new methods of modelling and predicting geotechnical design parameters. Neural technology is an emerging field of artificial intelligence that has attracted the interest of many scientists and engineers. They are information-processing systems that can mimic the biological system of the brain and can be trained to complete and classify input patterns, or to complete a function of their input. In this project the data available from the database are used to train Neural Networks to classify glacial tills according to their geotechnical properties and investigate their potential in predicting geotechnical design parameters