This thesis describes a system which is able to track the pose of a hand-held camera as it moves around a scene. The system builds a 3D map of point landmarks in the world while tracking the pose of the camera relative to this map using a process called simultaneous localisation and mapping (SLAM). To achieve real-time performance, the map must be kept sparse, but rather than observing only the mapped landmarks like previous systems, observations are made of features across the entire image. Their deviation from the predicted epipolar geometry is used to further constrain the estimated inter-frame motion and so improves the overall accuracy. The consistency of the estimation is also improved by performing the estimation in a camera-centred coordinate frame. As with any such system, tracking failure is inevitable due to occlusion or sudden motion of the camera. A relocalisation module is presented which monitors the SLAM system, detects tracking failure, and then resumes tracking as soon as the conditions have improved. This relocalisation process is achieved using a new landmark recognition algorithm which is trained on-line and provides high recall and a fast recognition time. The relocalisation module can also be used to achieve place recognition for a loop closure detection system. By taking into account both the geometry and appearance information when determining a loop closure this module is able to outperform previous loop closure detection techniques used in monocular SLAM. After recognising an overlap, the map is then corrected using a novel trajectory alignment technique that is able to cope with the inherent scale ambiguity in monocular SLAM. By incorporating all of these new techniques, the system presented can perform as a robust augmented reality system, or act as a navigation tool which could be used on a mobile robot in indoor and outdoor environments.

Industrial ecology is a field of study that encourages the use of closed-loop material cycles to achieve sustainability. Loop closing requires the movement of materials over space, and has long been practiced in the iron and steel industry. Iron and steel (ferrous) scrap generated in the U.S. is increasingly exported to countries in Asia, lengthening the transportation distance associated with closing the loop on the iron and steel life cycle. In order to understand the environmental cost of transporting this commodity, an activity-based maritime transportation model and a theoretical in-land transportation model are used to estimate emissions generated. Results indicate that 10.4 mmt of total emissions were generated, and emissions increased by 136 percent from 2004 to 2009. Increases in the amount of emissions generated are due to increases in the amount of scrap exported and distance it is transported.

<p><a>It is essential for a mobile robot during autonomous navigation to be able to detect revisited places or loop closures while performing Vision Simultaneous Localization And Mapping (VSLAM). Loop closing has been identified as one of the critical data association problem when building maps. It is an efficient way to eliminate errors and improve the accuracy of the robot localization and mapping. In order to solve loop closing problem, the ORB-SLAM algorithm, a feature based simultaneous localization and mapping system that operates in real time is used. This system includes loop closing and relocalization and allows automatic initialization. </a></p> <p>In order to check the performance of the algorithm, the monocular and stereo and RGB-D cameras are used. The aim of this thesis is to show the accuracy of relocalization and loop closing process using ORB SLAM algorithm in a variety of environmental settings. The performance of relocalization and loop closing in different challenging indoor scenarios are demonstrated by conducting various experiments. Experimental results show the applicability of the approach in real time application like autonomous navigation.</p>

Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, but with recent advances in mobile performance visual SLAM is entering the consumer market in a completely new way. A visual SLAM system will however be sensitive to non cautious use that may result in severe motion, occlusion or poor surroundings in terms of visual features that will cause the system to temporarily fail. The procedure of recovering from such a fail is called relocalization. Together with two similar problems localization, to find your position in an existing SLAM session, and loop closing, the online reparation and perfection of the map in an active SLAM session, these can be grouped as visual location recognition (VLR). This thesis presents novel results by combining the scalability of FabMap and the precision of 13th Lab's tracking yielding high-precision VLR, +/- 10 cm, while maintaining above 99 % precision and 60 % recall for sessions containing thousands of images. Everything functional purely on a normal mobile phone. The applications of VLR are many. Indoors, where GPS is not functioning, VLR can still provide positional information and navigate you through big complexes like airports and museums. Outdoors, VLR can improve the precision of GPS tenfold yielding a new level of navigational experience. Virtual and augmented reality applications are other areas that benefit from improved positioning and localization.