Capturing Village-level Heritages with a Hand-held Camera-Laser Fusion Sensor

Abstract

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.

2D-3D camera fusion for visual odometry in outdoor environments

“…Image taken at a loop closing point with only one tree at the corner (top-right). Map built before (red) and after (white) the visit around the loop using 2D-2D based refinement [6] (bottom-left). Refined map obtained using our method (bottom-right).…”

“…With the relatively recent proliferation of affordable 3D and 2D sensors, many mobile robots are, or can easily be, equipped with both 2D and 3D cameras [1][2] [4][5] [6]. Most of such robots localize themselves using the Iterative Closest Point (ICP) algorithm (or one of its variants).…”

“…In practice, making such small loops while building large maps is undoubtedly a burden for the task at hand and often impossible. While incorporating information from extra sensors such as GPS has been proposed [6] [8], it is often argued that such information is neither accurate nor reliable enough. It is highly recommended, when building large maps, to perform the loop closing with large real loops, whenever possible, thus reducing the accumulation error.…”

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

“…Human-mounted mobile mapping systems have also been successfully applied to outdoor environments [13,14]. Fusing the data from a GPS unit, cameras, and LRFs, the authors Bok et al demonstrated large scale mapping of outdoor cultural heritage sites [13].…”

“…Fusing the data from a GPS unit, cameras, and LRFs, the authors Bok et al demonstrated large scale mapping of outdoor cultural heritage sites [13]. By relying GPS information to limit accumulated errors, the authors' proposed system is able to accurately map trajectories of many kilometers with little to no manual intervention.…”