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11	Fusão de informações obtidas a partir de múltiplas imagens visando à navegação autônoma de veículos inteligentes em abiente agrícola / Data fusion obtained from multiple images aiming the navigation of autonomous intelligent vehicles in agricultural environment Utino, Vítor Manha 08 April 2015 (has links) Este trabalho apresenta um sistema de auxilio à navegação autônoma para veículos terrestres com foco em ambientes estruturados em um cenário agrícola. É gerada a estimativa das posições dos obstáculos baseado na fusão das detecções provenientes do processamento dos dados de duas câmeras, uma estéreo e outra térmica. Foram desenvolvidos três módulos de detecção de obstáculos. O primeiro módulo utiliza imagens monoculares da câmera estéreo para detectar novidades no ambiente através da comparação do estado atual com o estado anterior. O segundo módulo utiliza a técnica Stixel para delimitar os obstáculos acima do plano do chão. Por fim, o terceiro módulo utiliza as imagens térmicas para encontrar assinaturas que evidenciem a presença de obstáculo. Os módulos de detecção são fundidos utilizando a Teoria de Dempster-Shafer que fornece a estimativa da presença de obstáculos no ambiente. Os experimentos foram executados em ambiente agrícola real. Foi executada a validação do sistema em cenários bem iluminados, com terreno irregular e com obstáculos diversos. O sistema apresentou um desempenho satisfatório tendo em vista a utilização de uma abordagem baseada em apenas três módulos de detecção com metodologias que não tem por objetivo priorizar a confirmação de obstáculos, mas sim a busca de novos obstáculos. Nesta dissertação são apresentados os principais componentes de um sistema de detecção de obstáculos e as etapas necessárias para a sua concepção, assim como resultados de experimentos com o uso de um veículo real. / This work presents a support system to the autonomous navigation for ground vehicles with focus on structured environments in an agricultural scenario. The estimated obstacle positions are generated based on the fusion of the detections from the processing of data from two cameras, one stereo and other thermal. Three modules obstacle detection have been developed. The first module uses monocular images of the stereo camera to detect novelties in the environment by comparing the current state with the previous state. The second module uses Stixel technique to delimit the obstacles above the ground plane. Finally, the third module uses thermal images to find signatures that reveal the presence of obstacle. The detection modules are fused using the Dempster-Shafer theory that provides an estimate of the presence of obstacles in the environment. The experiments were executed in real agricultural environment. System validation was performed in well-lit scenarios, with uneven terrain and different obstacles. The system showed satisfactory performance considering the use of an approach based on only three detection modules with methods that do not prioritize obstacle confirmation, but the search for new ones. This dissertation presents the main components of an obstacle detection system and the necessary steps for its design as well as results of experiments with the use of a real vehicle. Autonomous Mobile robots Calibração de câmeras Camera calibration Câmera estéreo Câmera monocular Câmera térmica Monocular camera Robôs móveis autônomos Sensor fusion and obstacle detection Stereo camera Thermal camera
12	Road Surface Preview Estimation Using a Monocular Camera Ekström, Marcus January 2018 (has links) Recently, sensors such as radars and cameras have been widely used in automotives, especially in Advanced Driver-Assistance Systems (ADAS), to collect information about the vehicle's surroundings. Stereo cameras are very popular as they could be used passively to construct a 3D representation of the scene in front of the car. This allowed the development of several ADAS algorithms that need 3D information to perform their tasks. One interesting application is Road Surface Preview (RSP) where the task is to estimate the road height along the future path of the vehicle. An active suspension control unit can then use this information to regulate the suspension, improving driving comfort, extending the durabilitiy of the vehicle and warning the driver about potential risks on the road surface. Stereo cameras have been successfully used in RSP and have demonstrated very good performance. However, the main disadvantages of stereo cameras are their high production cost and high power consumption. This limits installing several ADAS features in economy-class vehicles. A less expensive alternative are monocular cameras which have a significantly lower cost and power consumption. Therefore, this thesis investigates the possibility of solving the Road Surface Preview task using a monocular camera. We try two different approaches: structure-from-motion and Convolutional Neural Networks.The proposed methods are evaluated against the stereo-based system. Experiments show that both structure-from-motion and CNNs have a good potential for solving the problem, but they are not yet reliable enough to be a complete solution to the RSP task and be used in an active suspension control unit. Road Surface Preview Computer Vision Depth Estimation Convolutional Neural Network CNN traffic safety monocular camera mono vision system mono camera Structure from motion sfm 3D Reconstruction Autonomous Driving Datorseende trafiksäkerhet djupuppskattning mono kamera 3D rekonstruktion autonoma fordon Signal Processing Signalbehandling
13	Fusão de informações obtidas a partir de múltiplas imagens visando à navegação autônoma de veículos inteligentes em abiente agrícola / Data fusion obtained from multiple images aiming the navigation of autonomous intelligent vehicles in agricultural environment Vítor Manha Utino 08 April 2015 (has links) Este trabalho apresenta um sistema de auxilio à navegação autônoma para veículos terrestres com foco em ambientes estruturados em um cenário agrícola. É gerada a estimativa das posições dos obstáculos baseado na fusão das detecções provenientes do processamento dos dados de duas câmeras, uma estéreo e outra térmica. Foram desenvolvidos três módulos de detecção de obstáculos. O primeiro módulo utiliza imagens monoculares da câmera estéreo para detectar novidades no ambiente através da comparação do estado atual com o estado anterior. O segundo módulo utiliza a técnica Stixel para delimitar os obstáculos acima do plano do chão. Por fim, o terceiro módulo utiliza as imagens térmicas para encontrar assinaturas que evidenciem a presença de obstáculo. Os módulos de detecção são fundidos utilizando a Teoria de Dempster-Shafer que fornece a estimativa da presença de obstáculos no ambiente. Os experimentos foram executados em ambiente agrícola real. Foi executada a validação do sistema em cenários bem iluminados, com terreno irregular e com obstáculos diversos. O sistema apresentou um desempenho satisfatório tendo em vista a utilização de uma abordagem baseada em apenas três módulos de detecção com metodologias que não tem por objetivo priorizar a confirmação de obstáculos, mas sim a busca de novos obstáculos. Nesta dissertação são apresentados os principais componentes de um sistema de detecção de obstáculos e as etapas necessárias para a sua concepção, assim como resultados de experimentos com o uso de um veículo real. / This work presents a support system to the autonomous navigation for ground vehicles with focus on structured environments in an agricultural scenario. The estimated obstacle positions are generated based on the fusion of the detections from the processing of data from two cameras, one stereo and other thermal. Three modules obstacle detection have been developed. The first module uses monocular images of the stereo camera to detect novelties in the environment by comparing the current state with the previous state. The second module uses Stixel technique to delimit the obstacles above the ground plane. Finally, the third module uses thermal images to find signatures that reveal the presence of obstacle. The detection modules are fused using the Dempster-Shafer theory that provides an estimate of the presence of obstacles in the environment. The experiments were executed in real agricultural environment. System validation was performed in well-lit scenarios, with uneven terrain and different obstacles. The system showed satisfactory performance considering the use of an approach based on only three detection modules with methods that do not prioritize obstacle confirmation, but the search for new ones. This dissertation presents the main components of an obstacle detection system and the necessary steps for its design as well as results of experiments with the use of a real vehicle. Calibração de câmeras Câmera estéreo Câmera monocular Câmera térmica Robôs móveis autônomos Autonomous Mobile robots Camera calibration Monocular camera Sensor fusion and obstacle detection Stereo camera Thermal camera
14	3D Object Detection based on Unsupervised Depth Estimation Manoharan, Shanmugapriyan 25 January 2022 (has links) Estimating depth and detection of object instances in 3D space is fundamental in autonomous navigation, localization, and mapping, robotic object manipulation, and augmented reality. RGB-D images and LiDAR point clouds are the most illustrative formats of depth information. However, depth sensors offer many shortcomings, such as low effective spatial resolutions and capturing of a scene from a single perspective. The thesis focuses on reproducing denser and comprehensive 3D scene structure for given monocular RGB images using depth and 3D object detection. The first contribution of this thesis is the pipeline for the depth estimation based on an unsupervised learning framework. This thesis proposes two architectures to analyze structure from motion and 3D geometric constraint methods. The proposed architectures trained and evaluated using only RGB images and no ground truth depth data. The architecture proposed in this thesis achieved better results than the state-of-the-art methods. The second contribution of this thesis is the application of the estimated depth map, which includes two algorithms: point cloud generation and collision avoidance. The predicted depth map and RGB image are used to generate the point cloud data using the proposed point cloud algorithm. The collision avoidance algorithm predicts the possibility of collision and provides the collision warning message based on decoding the color in the estimated depth map. This algorithm design is adaptable to different color map with slight changes and perceives collision information in the sequence of frames. Our third contribution is a two-stage pipeline to detect the 3D objects from a monocular image. The first stage pipeline used to detect the 2D objects and crop the patch of the image and the same provided as the input to the second stage. In the second stage, the 3D regression network train to estimate the 3D bounding boxes to the target objects. There are two architectures proposed for this 3D regression network model. This approach achieves better average precision than state-of-theart for truncation of 15% or fully visible objects and lowers but comparable results for truncation more than 30% or partly/fully occluded objects. info:eu-repo/classification/ddc/000 ddc:000
15	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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