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. 2022 May 12;8(5):134.
doi: 10.3390/jimaging8050134.

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

Ramamoorthy Luxman  1 , Yuly Emilia Castro  1 , Hermine Chatoux  1 , Marvin Nurit  1 , Amalia Siatou  1   2 , Gaëtan Le Goïc  1 , Laura Brambilla  2 , Christian Degrigny  2 , Franck Marzani  1 , Alamin Mansouri  1
Affiliations

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

Ramamoorthy Luxman et al. J Imaging. .

Abstract

Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, limitations on the size of objects being acquired, speed, and portability. This paper presents a novel robotic arm-based system design, which we call LightBot, as well as its applications in reflectance transformation imaging (RTI) in particular. The proposed model alleviates some of the limitations observed in the case of free-form or dome-based systems. It allows the automation and reproducibility of one or a series of acquisitions adapting to a given surface in two-dimensional space.

Keywords: RTI stitching; cultural heritage; reflectance transformation imaging (RTI); robotic acquisition.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Reflectance transformation imaging technique. An example of the per pixel reflectance modeling using is shown here (a) PTM, (b) HSH and (c) DMD. These modelling plots are reprinted with permission from Pitard et al. [4].
Figure 2
Figure 2
RTI acquisition systems. (a) Free-form highlight-RTI (a acquisition performed by our group), (b) Dome system [14], (c) Proposed system—LightBot.
Figure 3
Figure 3
System architecture of the LightBot.
Figure 4
Figure 4
Visualization of the system in ROS RViz. (a) Visualization of the system and an example set of light poses in Rviz. (b) Reference frames to which the system components are attached.
Figure 5
Figure 5
Planning of motion between two points with collision avoidance and optimal trajectory. (a) Planned sequence of the robot poses during its motion from the starting pose to the target pose. (b) Optimal valid path having the lowest cost vs. the shortest path.
Figure 6
Figure 6
Dome configurations with variable radius. The radius of the dome can be adjusted from a few cm to 30 cm with this robot arm. (a) Hemisphere radius = 15 cm. (b) Hemisphere radius = 20 cm. (c) Hemisphere radius = 25 cm.
Figure 7
Figure 7
Relighting of the surface from RTI of the surface captured with dome of different sizes. In this example, the surface is relighted from θ=90∘, ϕ=45∘ and from θ=−90∘, ϕ=45∘.
Figure 8
Figure 8
RTI acquisitions of a canvas painting (24.5 cm × 20 cm) in parts. There are in total 6 acquisitions, each covering an area of 9.3 cm × 7.6 cm with 30 % overlap between each pair of consecutive acquisitions.
Figure 9
Figure 9
The acquired data are stitched to reconstruct the whole canvas painting. Visualization of the relighted image, normal map and directional slope obtained by processing of the RTI data using DMD [4] model fitting. (a) Relighted image. (b) Normal map.
Figure 10
Figure 10
An example of batch acquisition where the system executes acquisition of 4 surfaces one after the other.
See this image and copyright information in PMC

References

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