Efficient Laser-Based 3D SLAM for Coal Mine Rescue Robots

Abstract

An accurate description of laneway space with self-localization is a key issue when coal mine rescue robots (CMRRs) perform post-disaster exploration and rescue missions. The 3D simultaneous localization and mapping (SLAM) is an effective but time-critical and highly challenging task in complex laneway scenarios, especially after disasters. In this paper, we propose a novel real-time 3D SLAM based on normally distributed transform (NDT) that employs pose graph optimization and loop closure to further improve mapping consistency. We innovatively extract floors and walls in the laneway as plane nodes to construct landmark constraints, in addition to applying pose nodes from the lidar odometry via NDT. A lightweight and effective loop detection method is conducted using odometry with an appearance-based approach to building a globally consistent map. The proposed method was evaluated on a public dataset, and field tests in an underground coal mine were performed. Results indicate that our algorithm can achieve lower computational complexity and drift, which can provide pose estimation and environment description for CMRRs to realize remote control assistance and automatic navigation in coal mine rescue missions.

LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

“…Two-dimensional LiDAR-based SLAMs are more computationally efficient than 3D ones. However, due to the limited number of dimensions, they are not able to provide the estimation of the vehicle’s pose on uneven ground with six degrees of freedom (DOF) [ 46 ]. Additionally, with the limited number of features, it is hard for this type of algorithm to adapt to long laneways, which are highly similar.…”

“…Different methods to restore 3D information from 2D point clouds can be used. Nevertheless, it makes the configuration complicated [ 46 ]. Therefore, in this work, we focus on testing 3D SLAM algorithms, which provide a more accurate estimation of the environment.…”

A LiDAR-Aided Inertial Positioning Approach for a Longwall Shearer in Underground Coal Mining

“…e light detection and ranging (LiDAR) technique is being increasingly used in the mining industry because of its excellent environment perception. Li et al [16] proposed the SLAM algorithm based on LiDAR, which could realize the remote control and automatic positioning of a mine rescue robot. Wu et al [17] examined the navigation during unmanned driving in a mine and realized the stable output of the tunnel map by using the SLAM algorithm based on LiDAR.…”

Remnant Coal Detection System

“…A previous CSIRO research effort related to the measurement of haul truck load volumes had demonstrated the efficacy of laser-scanning (lidar) technologies for industrial load monitoring [4], with the resulting system successfully commercialised for the case of mining haul trucks. Related research in the area of three-dimensional (3D) mapping within coal mines [5,6] has validated the application of laser-scanning sensors to the problem of volume estimation of coal targets. Accurate volume and/or surface area mapping of coal targets using lidar technology requires careful selection and configuration of measuring instruments combined with an effective algorithm to deal with complicating factors such as target aspect and related shadowing effects [7], vehicle motion [8], and alterations in vehicle velocity [9].…”