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• W2033620382 abstract "A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments." @default.
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• W2033620382 date "2014-12-01" @default.
• W2033620382 modified "2023-10-16" @default.
• W2033620382 title "Sensor based adaptive metric-topological cell decomposition method for semantic annotation of structured environments" @default.
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• W2033620382 doi "https://doi.org/10.1109/icarcv.2014.7064584" @default.
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