A surface reconstruction method for in-detail underwater 3D optical mapping. (January 2015)

• Record Type: Journal Article
• Authors: Campos, Ricard; Garcia, Rafael; Alliez, Pierre; Yvinec, Mariette
• Is Part Of: International journal of robotics research. Volume 34:Number 1(2015:Jan.)
• Page Start: 64
• Page End: 89
• Publication Date: 2015-01
• Subjects: Surface reconstruction -- outlier-rejection -- noise attenuation -- underwater robotics; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364914544531
• Languages: English
• ISSNs: 0278-3649

Abstract

Underwater range scanning techniques are starting to gain interest in underwater exploration, providing new tools to represent the seafloor. These scans (often) acquired by underwater robots usually result in an unstructured point cloud, but given the common downward-looking or forward-looking configuration of these sensors with respect to the scene, the problem of recovering a piecewise linear approximation representing the scene is normally solved by approximating these 3D points using a heightmap (2.5D). Nevertheless, this representation is not able to correctly represent complex structures, especially those presenting arbitrary concavities normally exhibited in underwater objects. We present a method devoted to full 3D surface reconstruction that does not assume any specific sensor configuration. The method presented is robust to common defects in raw scanned data such as outliers and noise often present in extreme environments such as underwater, both for sonar and optical surveys. Moreover, the proposed method does not need a manual preprocessing step. It is also generic as it does not need any information other than the points themselves to work. This property leads to its wide application to any kind of range scanning technologies and we demonstrate its versatility by using it on synthetic data, controlled laser scans, and multibeam sonar surveys. Finally, and given the unbeatable level of detail that optical methods can provide, we analyze the application of this method on optical datasets related to biology, geology and archeology.