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Preserving a heritage as a digital archive is as important as preserving its physical structure. The digital preservation is essential for massive heritages which are often defenceless against various types of destruction and require frequent restorations. However, capturing heritages gets exceedingly harder as their scale grows. In this paper, we present a novel approach to reconstruct a massive-scale structure using a hand-held fusion sensor system. The approach includes new methods on calibration, motion estimation, and accumulated error reduction. The proposed sensor system consists of four cameras and two 2D laser scanners to obtain a wide field-of-view. A new calibration method successfully achieves a much lower reprojection error compared to the previous method. A motion estimation method provides accurate and robust relative poses by fully utilizing plenty observations. At the last stage, the accumulated error reduction removes the drift occurred over tens of thousands frames by adopting weak GPS prior and loop closing. Therefore the system is able to capture and geo-register large heritage architectures of square kilometers size. Furthermore, because no assumption or restriction is made, the user can freely move the system and can control the level of detail of the digital heritage without any effort. To demonstrate the performance, we have captured several important Korean heritages including Gyeongbok-Gung, the royal palace of Korea. The experimental result shows that the estimated route fits Google's satellite image and DGPS data while the detailed appearances of representative constructions are captured and preserved well.

We present a calibration method of a time-of-flight (ToF) sensor and a color camera pair to align the 3D measurements with the color image correctly. We have designed a 2.5D pattern board with irregularly placed holes to be accurately detected from low resolution depth images of a ToF camera as well as from high resolution color images. In order to improve the accuracy of the 3D measurements of a ToF camera, we propose to perform ray correction and range bias correction. We reset the transformation of the ToF sensor which transforms the radial distance into the scene depth in Cartesian coordinate through ray correction. Then we capture a planar scene from different depths to correct the distance error that is shown to be dependent not only on the distance but also on the pixel location. The range error profiles along the calibrated distance are classified according to their wiggling shapes and each cluster of profiles with similar shape are separately estimated using a B-spline function. The standard deviation of the remaining random noise is recorded as an uncertainty information of distance measurements. We show the performance of our calibration method quantitatively and qualitatively on various datasets, and validate the impact of our method by demonstrating an RGB-D shape refinement application.

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