An Alignment Method for the Integration of Underwater 3D Data Captured by a Stereovision System and an Acoustic Camera
| Autor: | Maurizio Muzzupappa, Fabio Bruno, Antonio Lagudi, Gianfranco Bianco |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Point cloud 02 engineering and technology optical and acoustic integration Remotely operated underwater vehicle lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry opto-acoustic vision 0202 electrical engineering electronic engineering information engineering Computer vision underwater 3D imaging lcsh:TP1-1185 Electrical and Electronic Engineering Visibility Instrumentation Rigid transformation Optimal estimation business.industry 010401 analytical chemistry Geometric transformation Atomic and Molecular Physics and Optics 0104 chemical sciences ROV navigation Noise Acoustic camera 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Sensors, Vol 16, Iss 4, p 536 (2016) Sensors (Basel, Switzerland) Sensors; Volume 16; Issue 4; Pages: 536 |
| ISSN: | 1424-8220 |
| Popis: | The integration of underwater 3D data captured by acoustic and optical systems is a promising technique in various applications such as mapping or vehicle navigation. It allows for compensating the drawbacks of the low resolution of acoustic sensors and the limitations of optical sensors in bad visibility conditions. Aligning these data is a challenging problem, as it is hard to make a point-to-point correspondence. This paper presents a multi-sensor registration for the automatic integration of 3D data acquired from a stereovision system and a 3D acoustic camera in close-range acquisition. An appropriate rig has been used in the laboratory tests to determine the relative position between the two sensor frames. The experimental results show that our alignment approach, based on the acquisition of a rig in several poses, can be adopted to estimate the rigid transformation between the two heterogeneous sensors. A first estimation of the unknown geometric transformation is obtained by a registration of the two 3D point clouds, but it ends up to be strongly affected by noise and data dispersion. A robust and optimal estimation is obtained by a statistical processing of the transformations computed for each pose. The effectiveness of the method has been demonstrated in this first experimentation of the proposed 3D opto-acoustic camera. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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