In Intelligent Transportation Systems, Advanced Vehicle Control and Safety System are one of the most important researches around the world. AVCSS is a technique applied on vehicle and is composed by sensor, computer, communication, and control. In order to keep driver safe, the technique covered Collision Avoidance, Longitudinal Automated Control, Lateral Automated Control, Automated Parking, etc, and Collision Avoidance, Longitudinal Automated Control, Lateral Automated Control are most important. This thesis implemented the Lateral Automated Control by using a CCD camera to extract the road environment. And I presented and analyzed lane detection about structured road and unstructured road. The structured road stands for its obvious lane mark such as general road and freeway; and the unstructured road stands for its unobvious lane mark or without lane mark such as country road and campus road. Because of the characteristic of lane mark, the structured road is easier to detect, and there were less research about unstructured road around the world. So this thesis focused on the unstructured lane detection, and implemented multi-system on DSP (Digital Signal Process). Finally, we applied intelligent control system to vehicle and successfully guided the vehicle in structure and unstructured road.

The main focus of the study is the implementation of a practical indoor localization and mapping algorithm for large scale, structured indoor environments. Building an incremental consistent map while also using it for localization is partially unsolved problem and of prime importance for mobile robot navigation. Within this framework, a combined method consisting of feature based scan matching and FastSLAM algorithm using LADAR and odometer sensor is presented. In this method, an improved data association and localization accuracy is achieved by feeding the SLAM module with better incremental pose information from scan matching instead of raw odometer output. This thesis presents the following contributions for indoor localization and mapping. Firstly a method combining feature based scan matching and FastSLAM is achieved. Secondly, improved geometrical relations are used for scan matching and also a novel method based on vector transformation is used for the calculation of pose difference. These are carefully studied and tuned based on localization and mapping performance failures encountered in different realistic LADAR datasets. Thirdly, in addition to position, orientation information usage in line segment and corner oriented data association is presented as an extension in FastSLAM module. v The method is tested with LADAR and odometer data taken from real robot platforms operated in different indoor environments. In addition to using datasets from the literature, own datasets are collected on Pioneer 3AT experimental robot platform. As a result, a real time working localization algorithm which is pretty successive in large scale, structured environments is achieved.

Recent advances in Light Detection and Ranging (LIDAR) technology and integration have resulted in vehicle-borne platforms for urban LIDAR scanning, such as Terrapoint Inc.'s TITAN system. Such technology has lead to an explosion in ground LIDAR data. The large size of such mobile urban LIDAR data sets, and the ease at which they may now be collected, has shifted the bottleneck of creating abstract urban models for Geographical Information Systems (GIS) from data collection to data processing. While turning such data into useful models has traditionally relied on human analysis, this is no longer practical. This thesis outlines a methodology for automatically recovering the necessary information to create abstract urban models from mobile urban LIDAR data using computer vision methods. As an integral part of the methodology, a novel scale-based interest operator is introduced (Di erence of Normals) that is e cient enough to process large datasets, while accurately isolating objects of interest in the scene according to real-world parameters. Finally a novel localized object recognition algorithm is introduced (Local Potential Well Space Embedding), derived from a proven global method for object recognition (Potential Well Space Embedding). The object recognition phase of our methodology is discussed with these two algorithms as a focus. / Thesis (Master, Computing) -- Queen's University, 2010-03-01 12:26:34.698

LADAR (Laser Detection and Ranging) systems constitue a direct extension of the conventional radar techniques. Because they operate at much shorter wavelengths, LADARs have the unique capability to generate 3D images of objects. These laser systems have many applications in both the civilian and the defence fields concerning target detection and identification. The extraction of these features depends on the processing algorithms, target properties and 3D images quality. In order to support future LADAR hardware device developments and system engineering studies, it is necessary to understand the influences of the phenomena leading to the final image. Hence, the modelling of the laser pulse, propagations effects, reflection properties, detection technique and receiver signal processinghave to be taken into account. A complete simulator has been developed consisting of a graphical user interface and a simulation program. The computer simulation produces simulated 3D images for a direct detection pulse LADAR under a wide variety of conditions. Each stage from the laser source to the 3D image generation has been modelled. It yields an efficient simulation tool which will be of help in the design of the future LADAR systems and gauge their performances. This master’s thesis contains the theoretical background about laser used to build the simulation program. The latter is described schematically in order to provide an insight for the reader. The graphical interface is then presented as a short user’s manual. Finally, in order to illustrate the possibilities of the simulator, a collection of selected simulations concludes the report.

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L'imagerie laser 3D est une technique performante utilisée notamment pour cartographier l'environnement dans lequel évolue un aéronef, en mesurant la distance le séparant d'un objet, en plus des coordonnées (x,y). Le système est capable d'acquérir des mesures par tout temps (nuit, pluie, brouillard). Une nouvelle génération de capteurs, multi-pixels et ultra-sensibles, permet alors de répondre aux besoins identifiés pour cartographier avec précision une zone de grande superficie : haute résolution spatiale, longue portée avec précision centimétrique et rapidité d'acquisition. Il s'agit des plans focaux 3D Geiger. Avant leur utilisation en aéroporté, il était nécessaire de se familiariser avec leur fonctionnement, basé sur les probabilités de détection. Un simulateur reproduisant l'ensemble de la chaîne d'acquisition à été développé, puis validé sur des cas réels, au sol et en conditions statiques. Il a ensuite permis de démontrer l'intérêt des plans focaux Geiger pour la cartographie aéroportée à longue distance. / 3D laser imaging is a powerful technique used to recognize the environment around an aircraft, by measuring the distance between the system and an objet, in addition to its spatial coordinates. Also, it allows data acquisition under any weather condition (night, rain, fog). A new kind of sensors, multi-pixel arrays with high sensitivity, seems in line with the needs of an accurate 3D mapping of a big area : high spatial resolution, longe range detection with centimeter accuracy and low acquisition time. There are 3D Geiger-mode focal plane arrays. Before an onboard use, it was necessary to analyze the operation of such a sensor, based on probabilities of detection. An end-to-end simulator reproducing the entire acquisition process has been implemented, then experimentally validated on ground and static cases. It finally allowed to demonstrate the potential and contribution of Geiger-mode focal plane arrays for long distance and high spatial resolution airborne 3D mapping.

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