672 research outputs found

    Data-driven Loop Closure Detection in Bathymetric Point Clouds for Underwater SLAM

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    Simultaneous localization and mapping (SLAM) frameworks for autonomous navigation rely on robust data association to identify loop closures for back-end trajectory optimization. In the case of autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES), data association is particularly challenging due to the scarcity of identifiable landmarks in the seabed, the large drift in dead-reckoning navigation estimates to which AUVs are prone and the low resolution characteristic of MBES data. Deep learning solutions to loop closure detection have shown excellent performance on data from more structured environments. However, their transfer to the seabed domain is not immediate and efforts to port them are hindered by the lack of bathymetric datasets. Thus, in this paper we propose a neural network architecture aimed to showcase the potential of adapting such techniques to correspondence matching in bathymetric data. We train our framework on real bathymetry from an AUV mission and evaluate its performance on the tasks of loop closure detection and coarse point cloud alignment. Finally, we show its potential against a more traditional method and release both its implementation and the dataset used

    Virtual 3D Reconstruction of Archaeological Pottery Using Coarse Registration

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    The 3D reconstruction of objects has not only improved visualisation of digitised objects, it has helped researchers to actively carry out archaeological pottery. Reconstructing pottery is significant in archaeology but is challenging task among practitioners. For one, excavated potteries are hardly complete to provide exhaustive and useful information, hence archaeologists attempt to reconstruct them with available tools and methods. It is also challenging to apply existing reconstruction approaches in archaeological documentation. This limitation makes it difficult to carry out studies within a reasonable time. Hence, interest has shifted to developing new ways of reconstructing archaeological artefacts with new techniques and algorithms. Therefore, this study focuses on providing interventions that will ease the challenges encountered in reconstructing archaeological pottery. It applies a data acquisition approach that uses a 3D laser scanner to acquire point cloud data that clearly show the geometric and radiometric properties of the object’s surface. The acquired data is processed to remove noise and outliers before undergoing a coarse-to-fine registration strategy which involves detecting and extracting keypoints from the point clouds and estimating descriptions with them. Additionally, correspondences are estimated between point pairs, leading to a pairwise and global registration of the acquired point clouds. The peculiarity of the approach of this thesis is in its flexibility due to the peculiar nature of the data acquired. This improves the efficiency, robustness and accuracy of the approach. The approach and findings show that the use of real 3D dataset can attain good results when used with right tools. High resolution lenses and accurate calibration help to give accurate results. While the registration accuracy attained in the study lies between 0.08 and 0.14 mean squared error for the data used, further studies will validate this result. The results obtained are nonetheless useful for further studies in 3D pottery reassembly

    Sensor Independent Deep Learning for Detection Tasks with Optical Satellites

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    The design of optical satellite sensors varies widely, and this variety is mirrored in the data they produce. Deep learning has become a popular method for automating tasks in remote sensing, but currently it is ill-equipped to deal with this diversity of satellite data. In this work, sensor independent deep learning models are proposed, which are able to ingest data from multiple satellites without retraining. This strategy is applied to two tasks in remote sensing: cloud masking and crater detection. For cloud masking, a new dataset---the largest ever to date with respect to the number of scenes---is created for Sentinel-2. Combination of this with other datasets from the Landsat missions results in a state-of-the-art deep learning model, capable of masking clouds on a wide array of satellites, including ones it was not trained on. For small crater detection on Mars, a dataset is also produced, and state-of-the-art deep learning approaches are compared. By combining datasets from sensors with different resolutions, a highly accurate sensor independent model is trained. This is used to produce the largest ever database of crater detections for any solar system body, comprising 5.5 million craters across Isidis Planitia, Mars using CTX imagery. Novel geospatial statistical techniques are used to explore this database of small craters, finding evidence for large populations of distant secondary impacts. Across these problems, sensor independence is shown to offer unique benefits, both regarding model performance and scientific outcomes, and in the future can aid in many problems relating to data fusion, time series analysis, and on-board applications. Further work on a wider range of problems is needed to determine the generalisability of the proposed strategies for sensor independence, and extension from optical sensors to other kinds of remote sensing instruments could expand the possible applications of this new technique
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