3D location estimation and tunnel mapping of autonomous driving robots through 3D point cloud registration on underground mine rampways

Abstract

In this study, we developed a three-dimensional (3D) location estimation and tunnel mapping system to locate an autonomous robot in the rampway of an underground mine using 3D point cloud registration. A 3D point cloud of the mine tunnel was measured using a 3D light detection and ranging (LiDAR) sensor and registered using the iterative closest point (ICP) algorithm to estimate the 3D pose of the robot. This was combined with two-dimensional LiDAR, inertial measurement unit, and encoder sensors to estimate the 3D trajectory of the robot. Additionally, the 3D tunnel mapping was performed using the 3D trajectory of the robot and the 3D point cloud data of the tunnel. A comparison of the tunnel maps created using conventional surveying equipment and the robot indicated a mapping error of 0.2275 m and localization error of 0.2465 m confirming the excellent overall tunnel mapping and localization performance. The tunnel mapping areas were further compared by selecting areas with relatively high and low ICP matching accuracies; the calculated errors were 0.6186 and 0.2257 m in the areas with low and high accuracies, respectively. Furthermore, the accuracy of the ICP matching tended to be low in areas where the change in the pitch angle of the robot was large.

Keywords

Location estimation / Tunnel mapping / Autonomous driving robot / Point cloud registration / Trajectory estimation / Mining safety

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Heonmoo Kim: Writing - original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Yosoon Choi: Writing - review & editing, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declaration of competing interest

Yosoon Choi is an editorial board member for Underground Space and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests..

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (Grant No. 2710003407).

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