Registering models is an essential building block of many robotic applications. In case of three-dimensional data, the models to be aligned usually consist of point clouds. In this letter, we propose a formalism to represent in a uniform manner scenes consisting of high-level geometric primitives, including lines and planes. Additionally, we derive both an iterative and a direct method to determine the transformation between heterogeneous scenes (solver). We analyzed the convergence behavior of this solver on synthetic data. Furthermore, we conducted comparative experiments on a full registration pipeline that operates on raw data, implemented on top of our solver. To this extent we used public benchmark datasets and we compared against state-of-the-art approaches. Finally, we provide an implementation of our solver together with scripts to ease the reproduction of the results presented in this letter.
Unified Representation and Registration of Heterogeneous Sets of Geometric Primitives / Nardi, Federico; Della Corte, Bartolomeo; Grisetti, Giorgio. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 4:2(2019), pp. 625-632. [10.1109/LRA.2019.2891989]
Unified Representation and Registration of Heterogeneous Sets of Geometric Primitives
Nardi, Federico
;Della Corte, Bartolomeo;Grisetti, Giorgio
2019
Abstract
Registering models is an essential building block of many robotic applications. In case of three-dimensional data, the models to be aligned usually consist of point clouds. In this letter, we propose a formalism to represent in a uniform manner scenes consisting of high-level geometric primitives, including lines and planes. Additionally, we derive both an iterative and a direct method to determine the transformation between heterogeneous scenes (solver). We analyzed the convergence behavior of this solver on synthetic data. Furthermore, we conducted comparative experiments on a full registration pipeline that operates on raw data, implemented on top of our solver. To this extent we used public benchmark datasets and we compared against state-of-the-art approaches. Finally, we provide an implementation of our solver together with scripts to ease the reproduction of the results presented in this letter.File | Dimensione | Formato | |
---|---|---|---|
Nardi_Postprint_Unified_2019
Open Access dal 11/01/2020
Note: https://ieeexplore.ieee.org/document/8607088
Tipologia:
Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
1.93 MB
Formato
Adobe PDF
|
1.93 MB | Adobe PDF | |
Nardi_Unified_2019.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
1.65 MB
Formato
Adobe PDF
|
1.65 MB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.