Global registration of subway tunnel point clouds using an augmented extended Kalman filter and central-axis constraint.

Zhizhong Kang,Jinlei Chen,Baoqian Wang

doi:10.1371/journal.pone.0126862

Zhizhong Kang, Jinlei Chen + Show 1 more

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Journal: PloS one	Publication Date: May 12, 2015
Citations: 5	License type: CC BY 4.0

Affiliation: China University of Geosciences (Beijing)

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Because tunnels generally have tubular shapes, the distribution of tie points between adjacent scans is usually limited to a narrow region, which makes the problem of registration error accumulation inevitable. In this paper, a global registration method is proposed based on an augmented extended Kalman filter and a central-axis constraint. The point cloud registration is regarded as a stochastic system, and the global registration is considered to be a process that recursively estimates the rigid transformation parameters between each pair of adjacent scans. Therefore, the augmented extended Kalman filter (AEKF) is used to accurately estimate the rigid transformation parameters by eliminating the error accumulation caused by the pair-wise registration. Moreover, because the scanning range of a terrestrial laser scanner can reach hundreds of meters, a single scan can cover a tunnel segment with a length of more than one hundred meters, which means that the central axis extracted from the scan can be employed to control the registration of multiple scans. Therefore, the central axis of the subway tunnel is first determined through the 2D projection of the tunnel point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm. Because the extraction of the central axis by quadratic curve fitting may suffer from noise in the tunnel points and from variations in the tunnel, we present a global extraction algorithm that is based on segment-wise quadratic curve fitting. We then derive the central-axis constraint as an additional observation model of AEKF to optimize the registration parameters between each pair of adjacent scans. The proposed approach is tested on terrestrial point clouds that were acquired in a subway tunnel. The results show that the proposed algorithm is capable of improving the accuracy of aligning multiple scans by 48%.

Highlights

Because underground structures, such as tunnels, require routine inspections and maintenance for their optimal use, efficient and accurate tunnel inspections are mandatory
This paper presents a design for an augmented extended Kalman filter (AEKF) for the global registration of tunnel point clouds
We proposed a global registration approach that is based on an augmented extended Kalman filter and central-axis constraints

Summary

Introduction

Because underground structures, such as tunnels, require routine inspections and maintenance for their optimal use, efficient and accurate tunnel inspections are mandatory. The central-axis constraint is derived as an additional observation model of the AEKF to optimize the registration parameters between each pair of adjacent scans. Because the extraction of the segments of the bounding lines and the central axis on the XOY plane using the three models may be affected by noise in the tunnel points, there may be deviations in the overlapping parts of adjacent fitted models (Fig 5).

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