Following text focuses on use of wireless technologies in OrthoPilot navigation system developed by B.Braun company. Description of OrthoPilot software is followed by overview of available wireless technologies highlighting their both advantages and disadvantages. Practical part consists of two main parts, mostly dealing with electronic circuits. First part describes development process of camera-wireless printed circuit board which substitutes currently used RS-422 cable connection between PC and stereo camera. Part of this chapter covers programming in C++ in order to make interface compatible with the rest of current OrthoPilot software. Second bigger part deals with remote controller development using prototyping board mikroMedia for XMEGA. Besides electrical circuits design, chapter describes also software part - microcontroller programming in C language. Thesis is concluded by discussing system limitations and ideas for future development.

Modern cars are often equipped with sensors like radar, infrared cameras and stereo cameras that collect information about its surroundings. By using a stereo camera, it is possible to receive information about the distance to points in front of the car. This information can be used to estimate the height of the predicted path of the car. An application which does this is the stereo based Road surface preview (RSP) algorithm. By using the output from the RSP algorithm it is possible to use active suspension control, which controls the vertical movement of the wheels relative to the chassis. This application primarily makes the driving experience more comfortable, but also extends the durability of the vehicle. The idea behind this Master’s thesis is to create an evaluation tool for the RSP algorithm, which can be used at arbitrary roads.  The thesis describes the proposed evaluation tool, where focus has been to make an accurate comparison of camera data received from the RSP algorithm and laser data used as ground truth in this thesis. Since the tool shall be used at the company proposing this thesis, focus has also been on making the tool user friendly. The report discusses the proposed methods, possible sources to errors and improvements. The evaluation tool considered in this thesis shows good results for the available test data, which made it possible to include an investigation of a possible improvement of the RSP algorithm.

De acordo com a Organização Mundial da Saúde,cerca de 1,2milhões de pessoas no mundo morrem em acidentes de trânsito. Sistemas de assistência ao motorista e veículos autônomos podem diminuir o número de acidentes. Dentre as várias demandas existentes para viabilizar essa tecnologia, sistemas computacionais de percepção ainda permanecem sem uma solução definitiva. Dois deles, detecção de obstáculos e de via navegável, normalmente fazem uso de algoritmos sofisticados como técnicas de aprendizado supervisionado, que mostram resultados impressionantes quando treinados com bases de dados bem definidas e diversificadas.Entretanto, construir, manter e atualizar uma base de dados com exemplos de vários lugares do mundo e em diversas situações é trabalhoso e complexo. Assim, métodos adaptativos e auto-supervisionados mostram-se como boas alternativas para sistemas de detecção do futuro próximo. Neste contexto, esta tese apresenta um método para estimar obstáculose via navegável através de sensores de baixo custo (câmeras estereoscópicas), sem o uso de técnicas de aprendizado de máquina e de diversas suposições normalmente utilizadas por trabalhos já disponíveis na literatura. Esses métodos utilizando sensor estereoscópico foram comparados fazendo uso de sensores do tipo 3D-LIDAR e mostraram resultados semelhantes. Este sistema poderá ser usado como uma fase pré-processamento de dados para melhorar ou viabilizar métodos adaptativos de aprendizado. / World wide, an estimated 1.2million lives are lostin road crashes each year and Advanced Driver Assistance Systems (ADAS) and Self-driving cars promise to reduce this number. Among the various issues to complete this technology, perception systems are still an unsolved issues. Normally two of them, obstacle detection and road detection, make use of sophisticated algorithms such as supervised machine learning methods which can perform with impressive results if it was trained with good data sets. Since it is a complex and an expensive job to create and maintain data bases of scenarios from the entire world, adaptive and/or self-supervised methods are good candidates for detection systems in the near future. Due that, this thesis present a method to estimate obsta- cles and estimate the road terrain using low cost sensors (stereo camera), avoiding supervised machine learning techniques and the most common assumptions used by works presented in literature. These methods were compared with 3D-LIDAR approaches achieving similar results and thus it can be used as a pre-processing step to improve or allow adaptive methods with machine learning systems.

<p>Robotic manipulators have been introduced in industry as a form of increasing productivity. Today, there exist an interest to enlarge the application of these manipulators to outdoor environments. Forestry cranes used in the forestry industry are a clear example. A long term goal in this industry is the development of autonomous systems to increase the logging efficiency. In this thesis, we consider how to control the rotator of these cranes, which is an electro-hydraulically actuated motor, and is used to control the end ffector tool. Control system design for the rotator is a challenging task since the sensing is not available to full extent. The main reason is the harsh environment that these machines are exposed to and sensors such as encoders are very fragile and cannot be used. In this thesis we use alternative sensing devices, such as a magnetic sensor and a stereo camera. In the case of the camera we face a problem with big delay. A prediction method has been used to compute desired values. Due to various reasons certain measuring devices can not be used in the industry. We consider four cases for control system design where dfferent combinations of available sensors have been used. Initially angular position of the rotator is controlled using only the magnetic sensor. A cascade control setup is used where pressure and position are measurable, first using the magnetic sensor and later using the camera. When only pressure measurements are available identified models have replaced sensors for position feedback. All tests and experiments have been done using a scaled version of a real forestry crane. The available crane has similar configuration and dynamics as the real one and is therefor useful for experimental purposes.</p>

In this master thesis a visual odometry system is implemented and explained. Visual odometry is a technique, which could be used on autonomous vehicles to determine its current position and is preferably used indoors when GPS is notworking. The only input to the system are the images from a stereo camera and the output is the current location given in relative position. In the C++ implementation, image features are found and matched between the stereo images and the previous stereo pair, which gives a range of 150-250 verified feature matchings. The image coordinates are triangulated into a 3D-point cloud. The distance between two subsequent point clouds is minimized with respect to rigid transformations, which gives the motion described with six parameters, three for the translation and three for the rotation. Noise in the image coordinates gives reconstruction errors which makes the motion estimation very sensitive. The results from six experiments show that the weakness of the system is the ability to distinguish rotations from translations. However, if the system has additional knowledge of how it is moving, the minimization can be done with only three parameters and the system can estimate its position with less than 5 % error.

Robotic manipulators have been introduced in industry as a form of increasing productivity. Today, there exist an interest to enlarge the application of these manipulators to outdoor environments. Forestry cranes used in the forestry industry are a clear example. A long term goal in this industry is the development of autonomous systems to increase the logging efficiency. In this thesis, we consider how to control the rotator of these cranes, which is an electro-hydraulically actuated motor, and is used to control the end ffector tool. Control system design for the rotator is a challenging task since the sensing is not available to full extent. The main reason is the harsh environment that these machines are exposed to and sensors such as encoders are very fragile and cannot be used. In this thesis we use alternative sensing devices, such as a magnetic sensor and a stereo camera. In the case of the camera we face a problem with big delay. A prediction method has been used to compute desired values. Due to various reasons certain measuring devices can not be used in the industry. We consider four cases for control system design where dfferent combinations of available sensors have been used. Initially angular position of the rotator is controlled using only the magnetic sensor. A cascade control setup is used where pressure and position are measurable, first using the magnetic sensor and later using the camera. When only pressure measurements are available identified models have replaced sensors for position feedback. All tests and experiments have been done using a scaled version of a real forestry crane. The available crane has similar configuration and dynamics as the real one and is therefor useful for experimental purposes.

Modern cars can be bought with camera systems that watch the road ahead. They can be used for many purposes, one use is to alert the driver when other cars are in the path of collision. If the warning system is to be reliable, the input data must be correct. One input can be the depth image from a stereo camera system; one reason for the depth image to be wrong is if the vergence angle between the cameras are erroneously calibrated. Even if the calibration is accurate from production there's a risk that the vergence changes due to temperature variations when the car is started. This thesis proposes one solution for short-time live calibration of a stereo camera system; where the speedometer data available on the CAN-bus is used as reference. The motion of the car is estimated using visual odometry, which will be affected by any errors in the calibration. The vergence angle is then altered virtually until the estimated speed is equal to the reference speed. The method is analyzed for noise and tested on real data. It is shown that detection of calibration errors down to 0.01 degrees is possible under certain circumstances using the proposed method.

Nowadays, 3D modelling of objects from multiple images is a topic that has gained great recognition and is widely used in various fields. Recently, lots of progress has been made in identification of people using 3D face models, which are usually reconstructed from multiple face images. In this thesis, a system including stereo cameras and structured light is built for the purpose of 3D modelling. The system outputs are 3D shapes of the face and also the texture information registered to this shape. Although the system in this thesis is developed for face reconstruction, it is not specific to faces. Using the same methodology proposed in this study 3D reconstruction of any object can be achieved.

To make three dimensional measurements using a stereo camera system, the intrinsic and extrinsic calibration of the system should be obtained. Furthermore, to allow zooming, intrinsic parameters should be re-estimated using only scene constraints. In this study both manual and autocalibration algorithms are implemented and tested. The implemented manual calibration system is used to calculate the parameters of the calibration with the help of a planar calibration object. The method is tested on different internal calibration settings and results of 3D measurements using the obtained calibration is presented. Two autocalibration methods have been implemented. The first one requires a general motion while the second method requires a pure rotation of the cameras. The autocalibration methods require point matches between images. To achieve a fully automated process, robust algorithms for point matching have been implemented. For the case of general motion the fundamental matrix relation is used in the matching algorithm. When there is only rotation between views, the homography relation is used. The results of variations on the autocalibration methods are also presented. The result of the manual calibration has been found to be very reliable. The results of the first autocalibration method are not accurate enough but it has been shown that the calibration from rotating cameras performs precise enough if rotation between images is sufficiently large.

De acordo com a Organização Mundial da Saúde,cerca de 1,2milhões de pessoas no mundo morrem em acidentes de trânsito. Sistemas de assistência ao motorista e veículos autônomos podem diminuir o número de acidentes. Dentre as várias demandas existentes para viabilizar essa tecnologia, sistemas computacionais de percepção ainda permanecem sem uma solução definitiva. Dois deles, detecção de obstáculos e de via navegável, normalmente fazem uso de algoritmos sofisticados como técnicas de aprendizado supervisionado, que mostram resultados impressionantes quando treinados com bases de dados bem definidas e diversificadas.Entretanto, construir, manter e atualizar uma base de dados com exemplos de vários lugares do mundo e em diversas situações é trabalhoso e complexo. Assim, métodos adaptativos e auto-supervisionados mostram-se como boas alternativas para sistemas de detecção do futuro próximo. Neste contexto, esta tese apresenta um método para estimar obstáculose via navegável através de sensores de baixo custo (câmeras estereoscópicas), sem o uso de técnicas de aprendizado de máquina e de diversas suposições normalmente utilizadas por trabalhos já disponíveis na literatura. Esses métodos utilizando sensor estereoscópico foram comparados fazendo uso de sensores do tipo 3D-LIDAR e mostraram resultados semelhantes. Este sistema poderá ser usado como uma fase pré-processamento de dados para melhorar ou viabilizar métodos adaptativos de aprendizado. / World wide, an estimated 1.2million lives are lostin road crashes each year and Advanced Driver Assistance Systems (ADAS) and Self-driving cars promise to reduce this number. Among the various issues to complete this technology, perception systems are still an unsolved issues. Normally two of them, obstacle detection and road detection, make use of sophisticated algorithms such as supervised machine learning methods which can perform with impressive results if it was trained with good data sets. Since it is a complex and an expensive job to create and maintain data bases of scenarios from the entire world, adaptive and/or self-supervised methods are good candidates for detection systems in the near future. Due that, this thesis present a method to estimate obsta- cles and estimate the road terrain using low cost sensors (stereo camera), avoiding supervised machine learning techniques and the most common assumptions used by works presented in literature. These methods were compared with 3D-LIDAR approaches achieving similar results and thus it can be used as a pre-processing step to improve or allow adaptive methods with machine learning systems.