2,308 research outputs found
Improving Performance of Spatial Network Queries
Spatial network queries, for example KNN or range, operate on systems where objects are constrained to locations on a network. Current spatial network query algorithms rely on forms of network traversal which have a high complexity proportional to the size of the network making, them poor for large real-world networks. In this thesis, an alternative method of approximating the results of spatial network queries with a high level of accuracy is introduced. Distances between network points are stored in an M-Tree index, a balanced tree index where metric distance determines data ordering. The M-Tree uses the chessboard metric on network points embedded in a higher dimensional space using tRNE. Using the M-Tree both KNN and range queries are computed more efficiently than network traversal. Error rates of the M-Tree are low, with accuracies of 97% possible on KNN queries and perfect accuracy with 2% extra results on range queries
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
Spatiotemporal Indexing With the M-Tree
Modern GIS applications for transportation and defense often require the ability to store the evolving positions of a large number of objects as they are observed in motion, and to support queries on this spatiotemporal data in real time. Because the M-Tree has been proven as an index for spatial network databases, we have selected it to be enhanced as a spatiotemporal index. We present modifications to the tree which allow trajectory reconstruction with fast insert performance and modifications which allow the tree to be built with awareness of the spatial locality of reference in spatiotemporal data
Trajectory Similarity Measurement: An Efficiency Perspective
Trajectories that capture object movement have numerous applications, in
which similarity computation between trajectories often plays a key role.
Traditionally, the similarity between two trajectories is quantified by means
of heuristic measures, e.g., Hausdorff or ERP, that operate directly on the
trajectories. In contrast, recent studies exploit deep learning to map
trajectories to d-dimensional vectors, called embeddings. Then, some distance
measure, e.g., Manhattan or Euclidean, is applied to the embeddings to quantify
trajectory similarity. The resulting similarities are inaccurate: they only
approximate the similarities obtained using the heuristic measures. As distance
computation on embeddings is efficient, focus has been on achieving embeddings
yielding high accuracy.
Adopting an efficiency perspective, we analyze the time complexities of both
the heuristic and the learning-based approaches, finding that the time
complexities of the former approaches are not necessarily higher. Through
extensive experiments on open datasets, we find that, on both CPUs and GPUs,
only a few learning-based approaches can deliver the promised higher
efficiency, when the embeddings can be pre-computed, while heuristic approaches
are more efficient for one-off computations. Among the learning-based
approaches, the self-attention-based ones are the fastest to learn embeddings
that also yield the highest accuracy for similarity queries. These results have
implications for the use of trajectory similarity approaches given different
application requirements
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