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Driving can be dangerous. Humans become inattentive when performing a monotonous task like driving. Also the risk implied while multi-tasking, like using the cellular phone while driving, can break the concentration of the driver and increase the risk of accidents. Others factors like exhaustion, nervousness and excitement affect the performance of the driver and the response time. Consequently, car manufacturers have developed systems in the last decades which assist the driver under various circumstances. These systems are called driver assistance systems. Driver assistance systems are meant to support the task of driving, and the field of action varies from alerting the driver, with acoustical or optical warnings, to taking control of the car, such as keeping the vehicle in the traffic lane until the driver resumes control. For such a purpose, the vehicle is equipped with on-board sensors which allow the perception of the environment and/or the state of the vehicle. Cameras are sensors which extract useful information about the visual appearance of the environment. Additionally, a binocular system allows the extraction of 3D information. One of the main requirements for most camera-based driver assistance systems is the accurate knowledge of the motion of the vehicle. Some sources of information, like velocimeters and GPS, are of common use in vehicles today. Nevertheless, the resolution and accuracy usually achieved with these systems are not enough for many real-time applications. The computation of ego-motion from sequences of stereo images for the implementation of driving intelligent systems, like autonomous navigation or collision avoidance, constitutes the core of this thesis. This dissertation proposes a framework for the simultaneous computation of the 6 degrees of freedom of ego-motion (rotation and translation in 3D Euclidean space), the estimation of the scene structure and the detection and estimation of independently moving objects. The input is exclusively provided by a binocular system and the framework does not call for any data acquisition strategy, i.e. the stereo images are just processed as they are provided. Stereo allows one to establish correspondences between left and right images, estimating 3D points of the environment via triangulation. Likewise, feature tracking establishes correspondences between the images acquired at different time instances. When both are used together for a large number of points, the result is a set of clouds of 3D points with point-to-point correspondences between clouds. The apparent motion of the 3D points between consecutive frames is caused by a variety of reasons. The most dominant motion for most of the points in the clouds is caused by the ego-motion of the vehicle; as the vehicle moves and images are acquired, the relative position of the world points with respect to the vehicle changes. Motion is also caused by objects moving in the environment. They move independently of the vehicle motion, so the observed motion for these points is the sum of the ego-vehicle motion and the independent motion of the object. A third reason, and of paramount importance in vision applications, is caused by correspondence problems, i.e. the incorrect spatial or temporal assignment of the point-to-point correspondence. Furthermore, all the points in the clouds are actually noisy measurements of the real unknown 3D points of the environment. Solving ego-motion and scene structure from the clouds of points requires some previous analysis of the noise involved in the imaging process, and how it propagates as the data is processed. Therefore, this dissertation analyzes the noise properties of the 3D points obtained through stereo triangulation. This leads to the detection of a bias in the estimation of 3D position, which is corrected with a reformulation of the projection equation. Ego-motion is obtained by finding the rotation and translation between the two clouds of points. This problem is known as absolute orientation, and many solutions based on least squares have been proposed in the literature. This thesis reviews the available closed form solutions to the problem. The proposed framework is divided in three main blocks: 1) stereo and feature tracking computation, 2) ego-motion estimation and 3) estimation of 3D point position and 3D velocity. The first block solves the correspondence problem providing the clouds of points as output. No special implementation of this block is required in this thesis. The ego-motion block computes the motion of the cameras by finding the absolute orientation between the clouds of static points in the environment. Since the cloud of points might contain independently moving objects and outliers generated by false correspondences, the direct computation of the least squares might lead to an erroneous solution. The first contribution of this thesis is an effective rejection rule that detects outliers based on the distance between predicted and measured quantities, and reduces the effects of noisy measurement by assigning appropriate weights to the data. This method is called Smoothness Motion Constraint (SMC). The ego-motion of the camera between two frames is obtained finding the absolute orientation between consecutive clouds of weighted 3D points. The complete ego-motion since initialization is achieved concatenating the individual motion estimates. This leads to a super-linear propagation of the error, since noise is integrated. A second contribution of this dissertation is a predictor/corrector iterative method, which integrates the clouds of 3D points of multiple time instances for the computation of ego-motion. The presented method considerably reduces the accumulation of errors in the estimated ego-position of the camera. Another contribution of this dissertation is a method which recursively estimates the 3D world position of a point and its velocity; by fusing stereo, feature tracking and the estimated ego-motion in a Kalman Filter system. An improved estimation of point position is obtained this way, which is used in the subsequent system cycle resulting in an improved computation of ego-motion. The general contribution of this dissertation is a single framework for the real time computation of scene structure, independently moving objects and ego-motion for automotive applications.
In the context of information theory, the term Mutual Information has first been formulated by Claude Elwood Shannon. Information theory is the consistent mathematical description of technical communication systems. To this day, it is the basis of numerous applications in modern communications engineering and yet became indispensable in this field. This work is concerned with the development of a concept for nonlinear feature selection from scalar, multivariate data on the basis of the mutual information. From the viewpoint of modelling, the successful construction of a realistic model depends highly on the quality of the employed data. In the ideal case, high quality data simply consists of the relevant features for deriving the model. In this context, it is important to possess a suitable method for measuring the degree of the, mostly nonlinear, dependencies between input- and output variables. By means of such a measure, the relevant features could be specifically selected. During the course of this work, it will become evident that the mutual information is a valuable and feasible measure for this task and hence the method of choice for practical applications. Basically and without the claim of being exhaustive, there are two possible constellations that recommend the application of feature selection. On the one hand, feature selection plays an important role, if the computability of a derived system model cannot be guaranteed, due to a multitude of available features. On the other hand, the existence of very few data points with a significant number of features also recommends the employment of feature selection. The latter constellation is closely related to the so called "Curse of Dimensionality". The actual statement behind this is the necessity to reduce the dimensionality to obtain an adequate coverage of the data space. In other word, it is important to reduce the dimensionality of the data, since the coverage of the data space exponentially decreases, for a constant number of data points, with the dimensionality of the available data. In the context of mapping between input- and output space, this goal is ideally reached by selecting only the relevant features from the available data set. The basic idea for this work has its origin in the rather practical field of automotive engineering. It was motivated by the goals of a complex research project in which the nonlinear, dynamic dependencies among a multitude of sensor signals should be identified. The final goal of such activities was to derive so called virtual sensors from identified dependencies among the installed automotive sensors. This enables the real-time computability of the required variable without the expenses of additional hardware. The prospect of doing without additional computing hardware is a strong motive force in particular in automotive engineering. In this context, the major problem was to find a feasible method to capture the linear- as well as the nonlinear dependencies. As mentioned before, the goal of this work is the development of a flexibly applicable system for nonlinear feature selection. The important point here is to guarantee the practicable computability of the developed method even for high dimensional data spaces, which are rather realistic in technical environments. The employed measure for the feature selection process is based on the sophisticated concept of mutual information. The property of the mutual information, regarding its high sensitivity and specificity to linear- and nonlinear statistical dependencies, makes it the method of choice for the development of a highly flexible, nonlinear feature selection framework. In addition to the mere selection of relevant features, the developed framework is also applicable for the nonlinear analysis of the temporal influences of the selected features. Hence, a subsequent dynamic modelling can be performed more efficiently, since the proposed feature selection algorithm additionally provides information about the temporal dependencies between input- and output variables. In contrast to feature extraction techniques, the developed feature selection algorithm in this work has another considerable advantage. In the case of cost intensive measurements, the variables with the highest information content can be selected in a prior feasibility study. Hence, the developed method can also be employed to avoid redundance in the acquired data and thus prevent for additional costs.