Recursive-Batch Estimation of Motion and Structure from Monocular Image Sequences |
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| Affiliation: | 1. Institute for Frontier Materials, Deakin University, Waurn Ponds 3216, Australia;2. Monash Centre for Additive Manufacturing, Monash University, Clayton 3168, Australia;3. Future Industries Institute, University of South Australia, Mawson Lakes 5095, Australia;1. Laboratory of Bulk Nanostructured Materials, Belgorod State University, Belgorod 308015, Russia;2. National Science Center “Kharkov Institute of Physics and Technology” NAS of Ukraine, Kharkov 61108, Ukraine;3. UES, Inc., 4401 Dayton-Xenia Rd., Dayton, OH 45432, USA;1. Fisheries and Oceans Canada, Northwest Atlantic Fisheries Center, 80 East White Hills, St. John’s, Newfoundland and Labrador A1C 5X1, Canada;2. Departamento Biología de Predadores Tope, Instituto Antártico Argentino. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina;3. Centre for Fisheries Ecosystems Research, Fisheries and Marine Institute, Memorial University of Newfoundland and Labrador, P.O. Box 4920, St. John’s, NL A1C 5R3, Canada;4. Environment and Climate Change Canada, 6 Bruce Street, Mount Pearl, NL A1N 4T3, Canada;5. School of Marine Science, Shanghai Ocean University, 999 Huchenghuan Road, Shanghai 201306, China;1. Institute of Virology, Hannover Medical School, Hannover, Germany;2. Center of Structural and Cell Biology in Medicine, Institute of Biochemistry, University of Lübeck, Lübeck, Germany;3. German Center for Infection Research (DZIF) partner site Hamburg-Lübeck-Borstel-Riems, Germany;4. Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany;5. Centre for Structural Systems Biology (CSSB), Hamburg, Germany |
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| Abstract: | This paper addresses the issue of optimal motion and structure estimation from monocular image sequences of a rigid scene. The new method has the following characteristics: (1) the dimension of the search space in the nonlinear optimization is drastically reduced by exploiting the relationship between structure and motion parameters; (2) the degree of reliability of the observations and estimates is effectively taken into account; (3) the proposed formulation allows arbitrary interframe motion; (4) the information about the structure of the scene, acquired from previous images, is systematically integrated into the new estimations; (5) the integration of multiple views using this method gives a large 2.5D visual map, much larger than that covered by any single view. It is shown also that the scale factor associated with any two consecutive images in a monocular sequence is determined by the scale factor of the first two images. Our simulation results and experiments with long image sequences of real world scenes indicate that the optimization method developed in this paper not only greatly reduces the computational complexity but also substantially improves the motion and structure estimates over those produced by the linear algorithms. |
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