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.

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

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Target tracking refers to the process of estimating the state of a moving object from remote and noisy measurements. In this thesis we consider the Bayesian filtering framework to perform target tracking under nonlinear models, a target moving in continuous time, and measurements that are available in discrete time intervals (known as continuous-discrete). The Bayesian filtering theory establishes the mathematical basis to obtain the posterior probability density function of the state, given the measurement history. This probability density function contains all the information required about the state of the target. It is well documented that there is no exact solution for posterior density under the models mentioned. Hence, the approximation of such density functions have been studied for over four decades. The literature demonstrates that this has led to the development of multiple filters. In target tracking, due to the remote sensing performed, an additional complication emerges. The measurements received are not always from the desired target and could have originated from unknown sources, thus making the tracking more difficult. This problem is known as a measurement origin uncertainty. Additionally to the filters, different methods have been proposed to address the measurement origin uncertainty due to its negative impact, which could cause a false track. Unfortunately, a final solution has yet to be achieved. The first proposal of this thesis is a new approximate Bayesian filter for continuous-discrete systems. The new filter is a higher accuracy version of the cubature Kalman filter. This filter is developed using a fifth-degree spherical radial cubature rule and the Ito-Taylor expansion of order 1.5 for dealing with stochastic differential equations. The second proposal is an improved version of the probabilistic data association method. The proposed method utilizes the maximum likelihood values for selecting the measurements that are used for the data association. In the first experiment, the new filter is tested in a challenging 3-dimensional turn model, demonstrating superiority over other existing filters. In a second and third experiments, the proposed data association method is tested for target tracking in a 2-dimensional scenarios under heavy measurement origin uncertainty conditions. The second and third experiments demonstrate the superiority of the proposed data association method compared to the probabilistic data association. / Thesis / Doctor of Philosophy (PhD)

Active sensors, such as radar, lidar, and sonar, emit signals into the environment and analyze the reflections to gather information such as distance, bearing, and, with more complex processing, shape and material. Conversely, passive sensors such as microphones and cameras, rely on signals produced by objects in the environment to collect data. This deprives the sensors of the ability to directly detect distance unless used in arrays, but affords them the benefits of being concealed and saving energy. In modern applications, we see active sensors filtering out any signals not originating from their transducers as if they were noise. However, contemporary research has shown that echolocating bats have the capability of taking advantage of both active and passive echolocation. By fusing the information a bat can gather from a conspecific's echoes with their own, it is suggested that more data may become available to the eavesdropping bat. Taking bioinspiration from these suggested abilities, we seek to explore the question of how fusing active and passive ultrasonic sensing may effect the information available to a robotic vehicle. Our first investigation was an experimental verification of the capabilities of a stereo sensor for passively tracking an ultrasonic sound source using limited a priori information about the target being tracked. Our results pos- itively supported a previous simulation study and showed that the Bayesian estimator was further able to recover from divergences due to hardware and software limitations. Break- ing from the limited assumptions of the previous work, we began a full investigation of the fusion of active and passive sensing with a numerical investigation of the effects of these sensing techniques on a robotic vehicle performing simultaneous localization and mapping (SLAM). The SLAM problem consists of robot that is placed in an unknown environment, which it proceeds to map and localize itself within. By ensonifying the environment with a stationary beacon, we compared the performance of the vehicle when using active, passive, and fused sensing strategies. Building upon previous numerical simulations, we found supporting evidence that, when information available through active sensing is limited, incorporating passive measurements improves the information available to the vehicle and may also improve the accuracy of its map and localization. Finally, we took the first step to fully realizing our initial goal by numerically investigating robotic eavesdropping on two dynamics vehicles. This work showed promising results for the continued investigation of fused sensing strategies and also highlighted the importance of formation control and landmark initialization. / Doctor of Philosophy / While the stereotype of bats being blind is a fallacy, it is true many species rely on their abilities of echolocation to navigate their surrounding environment. It has been observed that bats not only use the echoes of their own vocalizations to gather this information but also may eavesdrop on the echoes of other bats in their immediate area. This suggests that bats have the ability to effectively use two types of sensing at once, which are categorized as active and passive sensing. Active sensors are defined by their need to create signals that are sent out to the environment while passive sensors rely on signals they can collect from the environment to understand it. In this work we investigate the question: can robots that combine active and passive sensing capabilities into a single sensor gain more effective information about their environment? The first problem we investigate is an experimental proof of concept that it is possible to passively track an acoustic emitter without direct knowledge of how it is moving. Using a simple two microphone sensor we show support for previously tested numerical results that this form of tracking is possible. Moving away from this constrained system our later work uses modeling and simulation of the simultaneous localization and mapping (SLAM) problem in robotics to gain more understanding of the question above.

With the booming development of WWW, many search engines have been developed to help users to find useful information from a great quantity of data. However, users may have different needs in different situations. Opposite to the Information Retrieval where users retrieve data actively, Information Filtering (IF) sends information from servers to passive users through broadcast mediums, rather than being searched by them. Therefore, each user has his (or her) profile stored in the database, where a profile records a set of interest items that can present his (or her) interests or habits. To efficiently store many user profiles in servers and filter irrelevant users, many signature-based index techniques are applied in IF systems. By using signatures, IF does not need to compare each item of profiles to filter out irrelevant ones. However, because signatures are incomplete information of profiles, it is very hard to answer the complex queries by using only the signatures. Therefore, a critical issue of the signature-based IF service is how to index the signatures of user profiles for an efficient filtering process. There are often two types of queries in the signature-based IF systems, the inexact filtering and the similarity search queries. In the inexact filtering, a query is an incoming document and it needs to find the profiles whose interest items are all included in the query. On the other hand, in the similarity search, a query is a user profile and it needs to find the users whose interest items are similar to the query user. In this thesis, we propose an ID-tree index strategy, which indexes signatures of user profiles by partitioning them into subgroups using a binary tree structure according to all of the different items among them. Basically, our ID-tree index strategy is a kind of the signature tree. In an ID-tree, each path from the root to a leaf node is the signature of the profile pointed by the leaf node. Because each profile is pointed only by one leaf node of the ID-tree, there will be no collision in the structure. In other words, there will be no two profiles assigned to the same signature. Moreover, only the different items among subgroups of profiles will be checked at one time to filter out irrelevant profiles for queries. Therefore, our strategy can answer the inexact filtering and the similarity search queries with less number of accessed profiles as compared to the previous strategies. Moreover, to build the index of signatures, it needs less time to batch a great deal of database profiles. From our simulation results, we show that our strategy can access less number of profiles to answer the queries than Chen's signature tree strategy for the inexact filtering and Aggarwal et al.'s SG-table strategy for the similarity search.

Ever since the rapid growth of the Internet, recommender systems have become essential in helping online users to search and retrieve relevant information they need. Just like the situation that people rely heavily on recommendation in their daily decision making processes, online users may identify desired documents more effectively and efficiently through recommendation of other users who exhibit similar interests, and/or through extracting crucial features of the users¡¦ past preferences. Typical recommendation approaches can be classified into collaborative filtering and content-based filtering. Both approaches, however, have their own drawbacks. The purpose of this research is thus to propose a hybrid approach for text recommendations. We combine collaborative input and document content to facilitate the creation of extended content-based user profiles. These profiles are then rearranged with the technique of latent semantic indexing. Two experiments are conducted to verify our proposed approach. The objective of these experiments is to compare the recommendation results from our proposed approach with those from the other two approaches. The results show that our approach is capable of distinguishing different degrees of document preference, and makes appropriate recommendation to users or does not make recommendation to users for uninterested documents. The application of our proposed approach is justified accordingly.