Place recognition based on landmarks or features is an important problem occurring in localization, mapping, computer vision and point cloud processing. In this paper, we present GLAROT-3D, a translation and rotation invariant 3D signature based on geometric relations. The proposed method encodes into a histogram the pairwise relative positions of keypoint features extracted from 3D sensor data. Since it relies only on geometric properties and not on specific feature descriptors, it does not require any prior training or vocabulary construction and enables lightweight comparisons between landmark maps. The similarity of two point maps is computed as the distance between the corresponding rotated histograms to achieve rotation invariance. Histogram rotation is enabled by efficient orientation histogram based on sphere cubical projection. The performance of GLAROT has been assessed through experiments with standard benchmark datasets.

Place Recognition of 3D Landmarks based on Geometric Relations / LODI RIZZINI, Dario. - 1:(2017), pp. 648-654. ((Intervento presentato al convegno IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) tenutosi a Vancouver (Canada) nel 24-28 Sept. 2017 [10.1109/IROS.2017.8202220].

Place Recognition of 3D Landmarks based on Geometric Relations

LODI RIZZINI DARIO
2017-01-01

Abstract

Place recognition based on landmarks or features is an important problem occurring in localization, mapping, computer vision and point cloud processing. In this paper, we present GLAROT-3D, a translation and rotation invariant 3D signature based on geometric relations. The proposed method encodes into a histogram the pairwise relative positions of keypoint features extracted from 3D sensor data. Since it relies only on geometric properties and not on specific feature descriptors, it does not require any prior training or vocabulary construction and enables lightweight comparisons between landmark maps. The similarity of two point maps is computed as the distance between the corresponding rotated histograms to achieve rotation invariance. Histogram rotation is enabled by efficient orientation histogram based on sphere cubical projection. The performance of GLAROT has been assessed through experiments with standard benchmark datasets.
978-1-5386-2682-5
Place Recognition of 3D Landmarks based on Geometric Relations / LODI RIZZINI, Dario. - 1:(2017), pp. 648-654. ((Intervento presentato al convegno IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) tenutosi a Vancouver (Canada) nel 24-28 Sept. 2017 [10.1109/IROS.2017.8202220].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2836798
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