• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.17-26
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• 2008

The 1st order level network of Korea was adjusted simultaneously in 1987. After that, the 1 st order level network of Korea was adjusted simultaneously by National Geographic Information Institute in 2006. The levelling data were acquired by digital level with invar staff from 2001 through 2006. The 1st order level network consists of 36 level lines. Among them, 34 level lines comprise 11 level loops. Among 36 level lines, 4 level lines have fore & back error larger than the regulations for the 1st order levelling of NGII, Korea. Also, the closing error of 3 loops of level network exceed the regulation for the 1st order levelling of NGII. The standard error of fore and back leveling between bench marks(${\eta}_1$) are distributed between 0.2 $mm/{\surd}km$ and 1.7 $mm/{\surd}km$. The standard error of loop closing(${\eta}_2$) is 2.0 $mm/{\surd}km$. This result means that the 1st order level network of Korea qualifies for the high precision leveling defined by International Geodetic Association in 1948. As the result of the 1st order level network adjustment, the reference standard error($\hat{{\sigma}_0}$) of the level network was 1.8 $mm/{\surd}km$, which is twice as good as that of the 1st adjustment of level networks in 1987.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.120-128
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• 2012

The loop closure problem is one of the most challenging issues in the vision-based simultaneous localization and mapping community. It requires the robot to recognize a previously visited place from current camera measurements. While the loop closure often relies on visual bag-of-words based on point features in the previous works, however, in this paper we propose a line-based method to solve the loop closure in the corridor environments. We used both the floor line and the anchored vanishing point as the loop closing feature, and a two-step loop closure algorithm was devised to detect a known place and perform the global pose correction. We propose an anchored vanishing point as a novel loop closure feature, as it includes position information and represents the vanishing points in bi-direction. In our system, the accumulated heading error is reduced using an observation of a previously registered anchored vanishing points firstly, and the observation of known floor lines allows for further pose correction. Experimental results show that our method is very efficient in a structured indoor environment as a suitable loop closure solution.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.72-81
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• 2023

We introduce tightly-coupled GNSS-LiDAR-Inertial state estimator, which is capable of SLAM (Simultaneously Localization and Mapping) and autonomous driving. Long term drift is one of the main sources of estimation error, and some LiDAR SLAM framework utilize loop closure to overcome this error. However, when loop closing event happens, one's current state could change abruptly and pose some safety issues on drivers. Directly utilizing GNSS (Global Navigation Satellite System) positioning information could help alleviating this problem, but accurate information is not always available and inaccurate vertical positioning issues still exist. We thus propose our method which tightly couples raw GNSS measurements into LiDAR-Inertial SLAM framework which can handle satellite positioning information regardless of its uncertainty. Also, with NLOS (Non-light-of-sight) satellite signal handling, we can estimate our states more smoothly and accurately. With several autonomous driving tests on AGV (Autonomous Ground Vehicle), we verified that our method can be applied to real-world problem.