The mean-squared error (MSE) metric is used to formulate an optimization problem that balances the influence of the geometry of the radio positions and the NLOS effects. In this work, we propose a novel algorithm that optimizes the UWB radio positions for a pre-defined region of interest in the presence of obstacles. Consequently, the placement of the UWB radios must be carefully designed to provide satisfactory localization accuracy for a region of interest. However, in cluttered environments, both the UWB radio positions and the obstacle-induced non-line-of-sight (NLOS) measurement biases significantly impact the quality of the position estimate. Robust Visual Localization of a UAV Over a Pipe-Rack Based on the Lie Group SE(3)Ībstract: Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has recently emerged as a promising indoor positioning solution. We validate the proposed method for homography estimation and visual localization. We show that the visual localization with feature points can be improved using our line features. Finally, we present the proposed line descriptor and matching in a Point and Line Localization (PL-Loc). Performing as group descriptors, the networks enhance line descriptors by understanding lines’ relative geometries. We also propose line signature networks sharing the line’s geometric attributes with neighborhoods. By attending to well-describable points on a line dynamically, our descriptor performs excellently on variable line lengths. Inspired by natural language processing (NLP) tasks where sentences can be understood and abstracted well in neural nets, we view a line segment as a sentence that contains points (words). In this paper, we effectively introduce Line-Transformers dealing with variable lines. Although recent convolutional neural network (CNN)-based line descriptors are promising for viewpoint changes or dynamic environments, we claim that the CNN architecture has innate disadvantages to abstract variable line length into the fixed dimensional descriptor. Keywords: Localization, Deep Learning for Visual Perception, SLAMĪbstract: Along with feature points for image matching, line features provide additional constraints to solve visual geometric problems in robotics and computer vision (CV).
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