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Loop closure for topological mapping and navigation with omnidirectional images

Abstract : Over the last three decades, research in mobile robotic mapping and localization has seen significant progress. However, most of the research projects these problems into the SLAM framework while trying to map and localize metrically. As metrical mapping techniques are vulnerable to errors caused by drift, their ability to produce consistent maps is limited to small scale environments. Consequently, topological mapping approaches which are independent of metrical information stand as an alternative to metrical approaches in large scale environments. This thesis mainly deals with the loop closure problem which is the crux of any topological mapping algorithm. Our main aim is to solve the loop closure problem efficiently and accurately using an omnidirectional imaging sensor. Sparse topological maps can be built by representing groups of visually similar images of a sequence as nodes of a topological graph. We propose a sparse / hierarchical topological mapping framework which uses Image Sequence Partitioning (ISP) to group visually similar images of a sequence as nodes which are then connected on occurrence of loop closures to form a topological graph. A hierarchical loop closure algorithm that can first retrieve the similar nodes and then perform an image similarity analysis on the retrieved nodes is used. An indexing data structure called Hierarchical Inverted File (HIF) is proposed to store the sparse maps to facilitate an efficient hierarchical loop closure. TFIDF weighting is combined with spatial and frequency constraints on the detected features for improved loop closure robustness. Sparsity, efficiency and accuracy of the resulting maps are evaluated and compared to that of the other two existing techniques on publicly available outdoor omni-directional image sequences. Modest loop closure recall rates have been observed without using the epi-polar geometry verification step common in other approaches. Although efficient, the HIF based approach has certain disadvantages like low sparsity of maps and low recall rate of loop closure. To address these shortcomings, another loop closure technique using spatial constraint based similarity measure on omnidirectional images has been proposed. The low sparsity of maps caused by over-partitioning of the input sequence has been overcome by using Vector of Locally Aggregated Descriptors (VLAD) for ISP. Poor resolution of the omnidirectional images causes fewer feature matches in image pairs resulting in reduced recall rates. A spatial constraint exploiting the omnidirectional image structure is used for feature matching which gives accurate results even with fewer feature matches. Recall rates better than the contemporary FABMAP 2.0 approach have been observed without the additional geometric verification. The second contribution of this thesis is the formulation of a visual memory management approach suitable for long term operability of mobile robots. The formulated approach is suitable for both topological and metrical visual maps. Initial results which demonstrate the capabilities of this approach have been provided. Finally, a detailed description of the acquisition and construction of our multi-sensor dataset is provided. The aim of this dataset is to serve the researchers working in the mobile robotics and vision communities for evaluating applications like visual SLAM, mapping and visual odometry. This is the first dataset with omnidirectional images acquired on a car-like vehicle driven along a trajectory with multiple loops. The dataset consists of 6 sequences with data from 11 sensors including 7 cameras, stretching 18 kilometers in a semi-urban environmental setting with complete and precise ground-truth.
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Hemanth Korrapati. Loop closure for topological mapping and navigation with omnidirectional images. Other. Université Blaise Pascal - Clermont-Ferrand II, 2013. English. ⟨NNT : 2013CLF22363⟩. ⟨tel-00877034⟩

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