What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

2020 ◽  
Vol 38 (1) ◽  
pp. 5-27 ◽  
Author(s):  
Adam Jacobson ◽  
Fan Zeng ◽  
David Smith ◽  
Nigel Boswell ◽  
Thierry Peynot ◽  
...  
Keyword(s):  
Underground Mining ◽  
Localization And Mapping ◽  
Mapping System ◽  
Mining Vehicles

scholarly journals The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3228 ◽  
Author(s):  
Yuwei Chen ◽  
Jian Tang ◽  
Changhui Jiang ◽  
Lingli Zhu ◽  
Matti Lehtomäki ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Simultaneous Localization And Mapping ◽  
Point Clouds ◽  
Medium Size ◽  
Accuracy Evaluation ◽  
Depth Cameras ◽  
Localization And Mapping ◽  
Mapping System ◽  
Small Footprint ◽  
Mapping Systems

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can’t meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS.

scholarly journals Dynamics of glass windows in mining vehicles under the impacts of rock pieces: numerical and analytical comparison for computational models validation

2021 ◽  
Vol 942 (1) ◽  
pp. 012009
Author(s):  
O Sukhanova ◽  
O Larin ◽  
B Ziętek
Keyword(s):  
Computational Models ◽  
Numerical Solutions ◽  
Underground Mining ◽  
Mesh Size ◽  
Work Conditions ◽  
Dynamics Analysis ◽  
Linear Dynamics ◽  
Safety Work ◽  
Models Validation ◽  
Mining Vehicles

Abstract This study represents the results of linear dynamics analysis of glass plates subjected to rock pieces impacts occurring in underground machines’ windows. The aim of the work is to provide analytical and numerical solutions, obtain frequencies and plate displacement, and compare results of stress calculation for different models. The work performs finite element method (FEM) computations within a modal analysis in 3D statement including a mesh-size convergence analysis. Given approach is a basement for estimation of safety work conditions for operators in cabins of underground mining vehicles when glass windows are subjected to rock bursts and damages.

Reactive navigation and opportunistic localization for autonomous underground mining vehicles

2002 ◽  
Vol 145 (1-2) ◽  
pp. 127-146 ◽  
Author(s):  
Jonathan M Roberts ◽  
Elliot S Duff ◽  
Peter I Corke
Keyword(s):  
Underground Mining ◽  
Reactive Navigation ◽  
Mining Vehicles

scholarly journals Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2915 ◽  
Author(s):  
Zhuli Ren ◽  
Liguan Wang ◽  
Lin Bi
Keyword(s):  
Point Cloud ◽  
Underground Mining ◽  
Mine Safety ◽  
Measurement Unit ◽  
3D Point Cloud ◽  
Point Cloud Registration ◽  
Mining Environment ◽  
Localization And Mapping ◽  
Key Frames ◽  
Low Efficiency

Unmanned mining is one of the most effective methods to solve mine safety and low efficiency. However, it is the key to accurate localization and mapping for underground mining environment. A novel graph simultaneous localization and mapping (SLAM) optimization method is proposed, which is based on Generalized Iterative Closest Point (GICP) three-dimensional (3D) point cloud registration between consecutive frames, between consecutive key frames and between loop frames, and is constrained by roadway plane and loop. GICP-based 3D point cloud registration between consecutive frames and consecutive key frames is first combined to optimize laser odometer constraints without other sensors such as inertial measurement unit (IMU). According to the characteristics of the roadway, the innovative extraction of the roadway plane as the node constraint of pose graph SLAM, in addition to automatic removing the noise point cloud to further improve the consistency of the underground roadway map. A lightweight and efficient loop detection and optimization based on rules and GICP is designed. Finally, the proposed method was evaluated in four scenes (such as the underground mine laboratory), and compared with the existing 3D laser SLAM method (such as Lidar Odometry and Mapping (LOAM)). The results show that the algorithm could realize low drift localization and point cloud map construction. This method provides technical support for localization and navigation of underground mining environment.

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