This thesis investigates the utilisation of cheap RGBD sensors in rigid body tracking and 3D multiview registration for augmented and Virtual reality applications. RGBD sensors can be used as an affordable substitute for the more sophisticated, but expensive, conventional laser-based scanning and tracking solutions. Nevertheless, the low-cost sensing technology behind them has several drawbacks such as the limited range, significant noisiness and instability. To deal with these issues, an innovative adaptation of Kalman filtering scheme is first proposed to improve the precision, smoothness and robustness of raw RGBD outputs. It also extends the native capabilities of the sensor to capture further targets. The mathematical foundations of such an adaptation are explained in detail, and its corrective effect is validated with real tracking as well as 3D reconstruction experiments. A Graphics Processing Unit (GPU) implementation is also proposed with the different optimisation levels in order to ensure real-time responsiveness. After extensive experimentation with RGBD cameras, a significant difference in accuracy was noticed between the newer and ageing sensors. This decay could not be restored with conventional calibration. Thus, a novel method for worn RGBD sensors correction is also proposed. Another algorithm for background/foreground segmentation of RGBD images is contributed. The latter proceeds through background subtraction from colour and depth images separately, the resulting foreground regions are then fused for a more robust detection. The three previous contributions are used in a novel approach for multiview vehicle tracking for mixed reality needs. The determination of the position regarding the vehicle is achieved in two stages: the former is a sensor-wise robust filtering algorithm that is able to handle the uncertainties in the system and measurement models resulting in multiple position estimates; the latter algorithm aims at merging the independent estimates by using a set of optimal weighting coefficients. The outcome of fusion is used to determine vehicle’s orientation in the scene. Finally, a novel recursive filtering approach for sparse registration is proposed. Unlike ordinary state of the art alignment algorithms, the proposed method has four advantages that are not available altogether in any previous solution. It is able to deal with inherent noise contaminating sensory data; it is robust to uncertainties related to feature localisation; it combines the advantages of both L2 , L (infinity) norms for a higher performance and prevention of local minima; it also provides an estimated rigid body transformation along with its error covariance. This 3D registration scheme is validated in various challenging scenarios with both synthetic and real RGBD data.

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The objective of this study is to develop an application of the Kalman filter for filtering and forecasting iceberg positions and velocities in order to calculate the risk of impact against a fixed structure or stationary vessel. Existing physical and probabilistic models are reviewed. Physical models are essentially based on the response of the iceberg to the forces acting on it. Statistical models forecast the motion of the iceberg based on past observations of the trajectory. A probabilistic iceberg trajectory model is used in this study so that uncertainties in the trajectory forecast can be explicitly included. The technique of Kalman filtering is described and applied to forecast future positions and velocities of an ice feature, including uncertainties.The trajectory forecast combined with a risk calculation, yields the probability of impact and the probability distribution of the time until impact. Numerical simulations are provided to demonstrate and validate the procedure. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

Orientador : Celso Pascoli Bottura / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-07-13T22:59:34Z (GMT). No. of bitstreams: 1 Quirino_RogerioBastos_M.pdf: 3562441 bytes, checksum: fd580d0c82d210f732c72d7856b2b7c1 (MD5) Previous issue date: 1990 / Resumo: Neste trabalho realizamos a computacão paralela de um algoritmo para filtragem ótima de sistemas dinamicos lineares interconectados, explorando eficientemente o paralelismo natural da estrutura de cálculo hierárquico, empregando multiprogramação com o sistema operacional Unix / Abstract: In this work the computational parallelization algorithm for optimum filtering of I arge scale systems is efficiently exploiting the natural parallelism there is hierarquical calculation structure, via multiprogramming on operational system Unix / Mestrado / Mestre em Engenharia Elétrica

Orientador: Manuel de Jesus Mendes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Campinas / Made available in DSpace on 2018-07-16T18:35:15Z (GMT). No. of bitstreams: 1 Tozzi_ClesioLuis_M.pdf: 2000518 bytes, checksum: 7d0f4de08f0f5b33cef8e3d057e17cb3 (MD5) Previous issue date: 1975 / Resumo: Não informado. / Abstract: Not informed. / Mestrado / Mestre em Automação

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Localization is an essential part of any autonomous vehicle. In a simple setting, the localization problem is almost trivial, and can be solved sufficiently using simple dead reckoning or an off-the-shelf GPS with differential corrections. However, as the surroundings become more complex, so does the localization problem. The urban environment is a prime example of a situation in which a vehicle's surroundings complicate the problem of position estimation. The urban setting is marked by tall structures, overpasses, and tunnels. Each of these can corrupt GPS satellite signals, or completely obscure them, making it impossible to rely on GPS alone. Dead reckoning is still a useful tool in this environment, but as is always the case, measurement and modeling errors inherent in dead reckoning systems will cause the position solution to drift as the vehicle travels eventually leading to a solution that is completely diverged from the true position of the vehicle. The most widely implemented method of combining the absolute and relative position measurements provided by GPS and dead reckoning sensors is the Extended Kalman Filter (EKF). The implementation discussed in this paper uses two Kalman Filters to track two completely separate position solutions. It uses GPS/INS and odometry to track the Absolute Position of the vehicle in the Global frame, and simultaneously uses odometry alone to compute the vehicle's position in an arbitrary Local frame. The vehicle is then able to use the Absolute position estimate to navigate on the global scale, i.e. navigate toward globally referenced checkpoints, and use the Relative position estimate to make local navigation decisions, i.e. navigating around obstacles and following lanes. This localization solution was used on team VictorTango's 2007 DARPA Urban Challenge entry, Odin. Odin successfully completed the Urban Challenge and placed third overall. / Master of Science

Small satellites are becoming increasingly appealing as technology advances and shrinks in both size and cost. The development time for a small satellite is also much less compared to a large satellite. For small satellites to be successful, the navigation systems must be accurate and very often they must be autonomous. For lunar navigation, contact with a ground station is not always available and the system needs to be robust. The extended Kalman filter is a nonlinear estimator that has been used on-board spacecraft for decades. The filter requires linear approximations of the state and measurement models. In the past few years, the unscented Kalman filter has become popular and has been shown to reduce estimation errors. Additionally, the Jacobian matrices do not need to be derived in the unscented Kalman filter implementation. The intent of this research is to explore the capabilities of the extended Kalman filter and the unscented Kalman filter for use as a navigation algorithm on small satellites. The filters are applied to a satellite orbiting the Moon equipped with an inertial measurement unit, a sun sensor, a star camera, and a GPS-like sensor. The position, velocity, and attitude of the spacecraft are estimated along with sensor biases for the IMU accelerometer, IMU gyroscope, sun sensor and star camera. The estimation errors are compared for the extended Kalman filter and the unscented Kalman filter for the position, velocity and attitude. The analysis confirms that both navigation algorithms provided accurate position, velocity and attitude. The IMU gyroscope bias was observable for both filters while only the IMU accelerometer bias was observable with the extended Kalman filter. The sun sensor biases and the star camera biases were unobservable. In general, the unscented Kalman filter performed better than the extended Kalman filter in providing position, velocity, and attitude estimates but requires more computation time. / text

A Discrete Extended Kalman Filter has been designed to process measurements from a magnetometer, sun sensor, IMU, and GPS receiver to provide the relative position, velocity, attitude, and gyro bias of a chaser spacecraft relative to a target spacecraft. An Extended Kalman Filter with Uncompensated Bias has also been developed for the implementation of well known biases and errors that are not directly observable. A detailed explanation of the algorithms, models, and derivations that go into both filters is presented. With this simulation and specific sensor selection the position of the chaser spacecraft relative to the target can be estimated to within about 5 m, the velocity to within .1 m/s, and the attitude to within 2 degrees for both filters. If a thrust is applied to the IMU measurements, it takes about 1.5 minutes to get a good position estimate, using the Extended Kalman Filter with Uncompensated Bias. The error settles almost immediately using the general Extended Kalman Filter. These filters have been designed for and can be implemented on almost any small, low cost, low power satellite with this inexpensive set of sensors. / text

An important aspect for precision guidance, navigation, and control for lunar operations is environmental modeling. In particular, consider gravity field modeling. Available gravity field models for the Moon reach degree and order 165 requiring the use and storage of approximately 26,000 spherical harmonic coefficients. Although the high degree and order provide a means by which to accurately predict trajectories within the influence of the Moon's gravitational field, the size of these models makes using them computationally expensive and restricts their use in design environments with limited computer memory and storage. It is desirable to determine reduced complexity realizations of the gravitational models to lower the computational burden while retaining the structure of the original gravitational field for use in rapid design environments. The extended Kalman filter and the unscented Kalman filter are used to create reduced order models and are compared against a simple truncation based reduction method. Both variations of the Kalman filter out perform the truncation based method as a means by which to reduce the complexity of the gravitational field. The extended Kalman filter and unscented Kalman filter were able to achieve good estimates of position while reducing the number of spherical harmonic coefficients used in gravitational acceleration calculations by approximately 5,400, greatly increasing the speed of the calculations while reducing the required computer allocation. / text