Browsing by Author "Gakne, Paul Verlaine"

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    Improving the Accuracy of GNSS Receivers in Urban Canyons using an Upward-Facing Camera
    (2018-07-03) Gakne, Paul Verlaine; O'Keefe, Kyle P. G.; Gao, Yang; Wang, Ruisheng; Fapojuwo, Abraham Olatunji; Ruotsalainen, Laura
    Global Navigation Satellite Systems are widely used as localization systems for various applications in indoor and outdoor environments. Autonomous vehicles for example rely on navigation sensors such as GNSS receivers, INS, odometers, LiDAR, radar, etc. However, none of these sensors alone is able to provide satisfactory position solutions in terms of accuracy, availability and reliability all the time and in all environments. This thesis presents a new tightly coupling method fusing the egomotion of a land vehicle estimated from a sky-pointing camera with GNSS signals and a digital map for navigation purposes in harsh urban canyon environments. The advantages of this configuration are three-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (known as segmentation). A satellite falling into the non-sky areas (e.g., buildings) will be rejected and not considered for the final position solution computation. Secondly, the narrow field of view sky-pointing camera is helpful for urban area egomotion estimation in the sense that it does not see most of the moving objects (e.g., cars) and thus is able to estimate the egomotion with fewer outliers than is typical with a forward-facing camera. Thirdly, the skyline can be extracted and serves as a finger print of the vehicle location in the city. This information can then be correlated with a 3D city model to obtain the vehicle location. In order to obtain an accurate solution from the proposed method, a few intermediate steps had to be taken into account. An improved image segmentation algorithm is presented. The output of this algorithm served for the skyline positioning and the camera-based multipath mitigation. Also, an accurate visual odometry was implemented. Moreover, the monocular-based visual odometry is able to determine the vehicle translation accurately but up to a scale only. An integrated system that tackles the scale factor issue is designed. From five datasets evaluated in this research, the proposed method has shown to be robust and provide more accurate position, velocity and attitude solution at least 83% of the time than the GNSS-only and loosely coupled GNSS/vision solutions.
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    Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
    (Multidisciplinary Digital Publishing Institute, 2018-04-17) Gakne, Paul Verlaine; O'Keefe, Kyle P.G.
    This paper presents a method of fusing the ego-motion of a robot or a land vehicle estimated from an upward-facing camera with Global Navigation Satellite System (GNSS) signals for navigation purposes in urban environments. A sky-pointing camera is mounted on the top of a car and synchronized with a GNSS receiver. The advantages of this configuration are two-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (also known as segmentation). A satellite falling into the non-sky areas (e.g., buildings, trees) will be rejected and not considered for the final position solution computation. Secondly, the sky-pointing camera (with a field of view of about 90 degrees) is helpful for urban area ego-motion estimation in the sense that it does not see most of the moving objects (e.g., pedestrians, cars) and thus is able to estimate the ego-motion with fewer outliers than is typical with a forward-facing camera. The GNSS and visual information systems are tightly-coupled in a Kalman filter for the final position solution. Experimental results demonstrate the ability of the system to provide satisfactory navigation solutions and better accuracy than the GNSS-only and the loosely-coupled GNSS/vision, 20 percent and 82 percent (in the worst case) respectively, in a deep urban canyon, even in conditions with fewer than four GNSS satellites.