This thesis addresses visual tracking of a non-cooperative as well as a partially cooperative satellite, to enable close-range rendezvous between a servicer and a target satellite. Visual tracking and estimation of relative motion between a servicer and a target satellite are critical abilities for rendezvous and proximity operation such as repairing and deorbiting. For this purpose, Lidar has been widely employed in cooperative rendezvous and docking missions. Despite its robustness to harsh space illumination, Lidar has high weight and rotating parts and consumes more power, thus undermines the stringent requirements of a satellite design. On the other hand, inexpensive on-board cameras can provide an effective solution, working at a wide range of distances. However, conditions of space lighting are particularly challenging for image based tracking algorithms, because of the direct sunlight exposure, and due to the glossy surface of the satellite that creates strong reflection and image saturation, which leads to difficulties in tracking procedures. In order to address these difficulties, the relevant literature is examined in the fields of computer vision, and satellite rendezvous and docking. Two classes of problems are identified and relevant solutions, implemented on a standard computer are provided. Firstly, in the absence of a geometric model of the satellite, the thesis presents a robust feature-based method with prediction capability in case of insufficient features, relying on a point-wise motion model. Secondly, we employ a robust model-based hierarchical position localization method to handle change of image features along a range of distances, and localize an attitude-controlled (partially cooperative) satellite. Moreover, the thesis presents a pose tracking method addressing ambiguities in edge-matching, and a pose detection algorithm based on appearance model learning. For the validation of the methods, real camera images and ground truth data, generated with a laboratory tet bed similar to space conditions are used. The experimental results indicate that camera based methods provide robust and accurate tracking for the approach of malfunctioning satellites in spite of the difficulties associated with specularities and direct sunlight. Also exceptional lighting conditions associated to the sun angle are discussed, aimed at achieving fully reliable localization system in a certain mission.

During the course of this thesis, two scenarios are considered. In the first one, we contribute to feature extraction algorithms. In the second one, we use features to improve object detection solutions and localization. The two scenarios give rise to into four thesis sub-goals. First, we present a new shape skeleton pruning algorithm based on contour approximation and the integer medial axis. The algorithm effectively removes unwanted branches, conserves the connectivity of the skeleton and respects the topological properties of the shape. The algorithm is robust to significant boundary noise and to rigid shape transformations. It is fast and easy to implement. While shape-based solutions via boundary and skeleton analysis are viable solutions to object detection, keypoint features are important for textured object detection. Therefore, we present a keypoint featurebased planar object detection framework for vision-based localization. We demonstrate that our framework is robust against illumination changes, perspective distortion, motion blur, and occlusions. We increase robustness of the localization scheme in cluttered environments and decrease false detection of targets. We present an off-line target evaluation strategy and a scheme to improve pose. Third, we extend planar object detection to a real-time approach for 3D object detection using a mobile and uncalibrated camera. We develop our algorithm based on two novel naive Bayes classifiers for viewpoint and feature matching that improve performance and decrease memory usage. Our algorithm exploits the specific structure of various binary descriptors in order to boost feature matching by conserving descriptor properties. Our novel naive classifiers require a database with a small memory footprint because we only store efficiently encoded features. We improve the feature-indexing scheme to speed up the matching process creating a highly efficient database for objects. Finally, we present a model-free long-term tracking algorithm based on the Kernelized Correlation Filter. The proposed solution improves the correlation tracker based on precision, success, accuracy and robustness while increasing frame rates. We integrate adjustable Gaussian window and sparse features for robust scale estimation creating a better separation of the target and the background. Furthermore, we include fast descriptors and Fourier spectrum packed format to boost performance while decreasing the memory footprint. We compare our algorithm with state-of-the-art techniques to validate the results.