Abstract
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.
Highlights
With the rapid development of ground unmanned driving and the harsh environment of deep resource exploitation, in order to improve the safety status of underground transportation operations and maximize the economic benefits of mining enterprises [1], the unmanned of underground mining environment is an inevitable trend in the future development
A lightweight and efficient loop detection and optimization based on rules and Generalized Iterative Closest Point (GICP) is designed, which is applied to correct motion drift in pose graph optimization
In order to further analyze the localization and mapping effect of the GICP-simultaneous localization and mapping (SLAM) algorithm proposed in this paper
Summary
With the rapid development of ground unmanned driving and the harsh environment of deep resource exploitation, in order to improve the safety status of underground transportation operations and maximize the economic benefits of mining enterprises [1], the unmanned of underground mining environment is an inevitable trend in the future development. The intelligent and precise localization of underground mining environment is the key. In the above localization and mapping methods, the corresponding auxiliary localization devices need to be installed in the underground environment. Due to the rough underground environment, it is easy to cause a cumulative error during the localization process. The experiment and and development platform of the scraper made according. Its is basically the same that as ofthat the of real consists of a according the real vehicle sizefunction. It consists bucket, a boom, a connecting rod, a rocker arm, a rotary cylinder, and a lifting cylinder. It realizes of a bucket, a boom, a connecting rod, a rocker arm, a rotary cylinder, and a lifting cylinder It realizes the the sight-seeing sight-seeing remote remote control of the scraper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
