Stepwise calibration of focused plenoptic cameras

Abstract

Monocular plenoptic cameras are slightly modified, off-the-shelf cameras that have novel capabilities as they allow for truly passive, high-resolution range sensing through a single camera lens. Commercial plenoptic cameras, however, are presently delivering range data in non-metric units, which is a barrier to novel applications e.g. in the realm of robotics. In this work we revisit the calibration of focused plenoptic cameras and bring forward a novel approach that leverages traditional methods for camera calibration in order to deskill the calibration procedure and to increase accuracy. First, we detach the estimation of parameters related to either brightness images or depth data. Second, we present novel initialization methods for the parameters of the thin lens camera model-the only information required for calibration is now the size of the pixel element and the geometry of the calibration plate. The accuracy of the calibration results corroborates our belief that monocular plenoptic imaging is a disruptive technology that is capable of conquering new markets as well as traditional imaging domains.

Benefits of plenoptic cameras for robot vision during close range on-orbit servicing maneuvers

“…Previous to the dataset recording, images of a checkerboard pattern were recorded with the plenoptic camera and used to perform the intrinsic, plenoptic, and extrinsic calibration with the procedure in [40]. If not stated otherwise, the RxLive software version 2.10 was used to process the recorded light fields and the derived data.…”

“…where h is the distance between the main lens principle plane and the MLA's principle plane [40] (cf. Fig.…”

“…A more practical approach was introduced by Strobl and Lingenauber [40], which only requires the camera's pixel size as prior knowledge. It is operated similar to established conventional camera calibration methods [44,45] but extended with an additional step for the calibration of the plenoptic parameters.…”

“…Sec. 4 -Depth estimation) are used to determine the plenoptic parameters B and h [40]. In combination with a robot arm, the extrinsic or hand-eye calibration is provided as well [45].…”

Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching

“…Ceiling and Wall Region. The camera model, which can change the three-dimensional object in the real world to twodimensional picture, is as follows [24,25]: There are two kinds of main indoor objects: ceiling and wall regions. The ceiling is parallel to the floor, and wall regions (e.g., wall, window, door, and furniture) are perpendicular to the floor.…”