Improved 3D sparse maps for high-performance SFM with low-cost omnidirectional robots

We consider the use of low-budget omnidirectional platforms for 3D mapping and self-localisation. These robots specifically permit rotational motion in the plane around a central axis, with negligible displacement. In addition, low resolution and compressed imagery, typical of the platform used, results in high level of image noise (_ ∽ 10). We observe highly sparse image feature matches over narrow inter-image baselines. This particular configuration poses a challenge for epipolar geometry extraction and accurate 3D point triangulation, upon which a standard structure from motion formulation is based. We propose a novel technique for both feature filtering and tracking that solves these problems, via a novel approach to the management of feature bundles. Noisy matches are efficiently trimmed, and the scarcity of the remaining image features is adequately overcome, generating densely populated maps of highly accurate and robust 3D image features. The effectiveness of the approach is demonstrated under a variety of scenarios in experiments conducted with low-budget commercial robots

Static scene illumination estimation from video with applications

We present a system that automatically recovers scene geometry and illumination from a video, providing a basis for various applications. Previous image based illumination estimation methods require either user interaction or external information in the form of a database. We adopt structure-from-motion and multi-view stereo for initial scene reconstruction, and then estimate an environment map represented by spherical harmonics (as these perform better than other bases). We also demonstrate several video editing applications that exploit the recovered geometry and illumination, including object insertion (e.g., for augmented reality), shadow detection, and video relighting

Vision-based navigation with reality-based 3D maps

This research is focused on developing vision-based navigation system for positioning and navigation in GPS degraded environments. The main research contributions are summarized as follows: a. A new concept of 3D map, which mainly consists of geo-referenced images, has been introduced. In this research, it provides the map-matching function for vision-based positioning. b. A method of vision-based positioning with use of photogrammetric methodologies has been proposed. It mainly obtains geometric information of the navigation environment from the 3D map through SIFT based image matching and uses photogrammetric space resection to solve the position in 6 degrees of freedom. The algorithms have been tested in an indoor environment. The accuracy has reached around 10 cm. c. A multi-level outlier detection scheme for the vision-based navigation system has been developed. It mainly combines RANSAC with data snooping. The former one deals with high percentage of mismatches, while data snooping removes outliers from different sources in the least squares adjustment for both 3D mapping and positioning solution. d. The deficiency of using RANSAC for outlier detection in image matching and homography estimation has been identified. In this research, a novel method which combines cross correlation with feature based image matching has been proposed. It is able to evaluate the RANSAC homography estimation and improve the image matching performance. The method has been successfully applied to the vision-based navigation solution to find corresponding view from the database and improve the final positioning accuracy. e. The positioning performance of the system has been evaluated through the analysis of mathematical model and experiments. The focus has been on various image matching conditions/methods and their impact on the system performance. The strength and weaknesses of the system have been revealed and investigated. f. The vision-based navigation system has been extended from indoor to outdoor with corresponding changes. Besides camera, it also takes advantage of multiple built-in sensors, including GPS receiver and a digital compass to assist visual methods in outdoor environments. Experiments demonstrate that such system can largely improve the position accuracy in areas where stand-alone GPS is affected and can be easily adopted on mobile devic

Three-dimensional scene recovery for measuring sighting distances of rail track assets from monocular forward facing videos

Rail track asset sighting distance must be checked regularly to ensure the continued and safe operation of rolling stock. Methods currently used to check asset line-of-sight involve manual labour or laser systems. Video cameras and computer vision techniques provide one possible route for cheaper, automated systems. Three categories of computer vision method are identified for possible application: two-dimensional object recognition, two-dimensional object tracking and three-dimensional scene recovery. However, presented experimentation shows recognition and tracking methods produce less accurate asset line-of-sight results for increasing asset-camera distance. Regarding three-dimensional scene recovery, evidence is presented suggesting a relationship between image feature and recovered scene information. A novel framework which learns these relationships is proposed. Learnt relationships from recovered image features probabilistically limit the search space of future features, improving efficiency. This framework is applied to several scene recovery methods and is shown (on average) to decrease computation by two-thirds for a possible, small decrease in accuracy of recovered scenes. Asset line-of-sight results computed from recovered three-dimensional terrain data are shown to be more accurate than two-dimensional methods, not effected by increasing asset-camera distance. Finally, the analysis of terrain in terms of effect on asset line-of-sight is considered. Terrain elements, segmented using semantic information, are ranked with a metric combining a minimum line-of-sight blocking distance and the growth required to achieve this minimum distance. Since this ranking measure is relative, it is shown how an approximation of the terrain data can be applied, decreasing computation time. Further efficiency increases are found by decomposing the problem into a set of two-dimensional problems and applying binary search techniques. The combination of the research elements presented in this thesis provide efficient methods for automatically analysing asset line-of-sight and the impact of the surrounding terrain, from captured monocular video.EThOS - Electronic Theses Online ServiceGBUnited Kingdo