Research on Point Cloud Registering Method of Tunneling Roadway Based on 3D NDT-ICP Algorithm

Yang, Jianjian; Wang, Chao; Luo, Wenjie; Zhang, Yuchen; Chang, Boshen; Wu, Miao

doi:10.3390/s21134448

Cited by 21 publications

(14 citation statements)

References 18 publications

(17 reference statements)

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“…Constraint relationships are established for the feature point cloud sets, and the 6-DOF pose transformation matrix is solved using the Levenberg-Marquardt (LM) optimization method. Subsequently, loop detection is conducted using the Iterative Closest Point (ICP) algorithm, and finally, the current point cloud is mapped to the global map based on graph optimization, completing the establishment of a high-precision map [15]. 1) Point cloud segmentation: Due to the complexity of the inspection environment and other factors, noise may exist in the laser point cloud data.…”

Section: Multi-line Lidar-based Slam Algorithm Design and Implementationmentioning

confidence: 99%

Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios

Wang¹,

Ma²,

Li³

2023

Preprint

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Factory safety inspections are crucial for ensuring safe production. However, manual inspections present issues such as low efficiency and high workload. Inspection robots provide an efficient and reliable solution for completing patrol tasks. The development of robot localization and path planning technologies provides guarantees for factory inspection robots to autonomously complete inspection tasks in complex environments. This paper studies mapping and localization, as well as path planning methods for robots in order to meet the application requirements of factory inspections. Two SLAM application systems based on multiple-line laser radar and vision are designed for different scenarios in consideration of the limitations of cameras and laser sensors in terms of their own characteristics and applicability in different environments. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that integrates the A-star algorithm and time elasticity band algorithm is proposed. This algorithm effectively solves the problem of path planning in complex environments that is prone to falling into local optimal solutions, thereby improving the inspection efficiency of robots. Experimental tests show that the designed SLAM and path planning methods can meet the needs of robot inspection in complex scenes and have good reliability and stability. The code used in this article is open source and can be accessed at https://github.com/Mxiii99/RSPP_CS.git.

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Section: Multi-line Lidar-based Slam Algorithm Design and Implementationmentioning

confidence: 99%

Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios

Wang¹,

Ma²,

Li³

2023

Preprint

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“…ere are many forks in the roadway, and the inner surface of the roadway is different with obstacles such as gravel, materials, electromechanical equipment, etc. [31,32]. e internal environment of the roadway is shown in Figure 3.…”

Section: Analysis Of Environmental Modeling Characteristics Of Excava...mentioning

confidence: 99%

Establishment of a Coal Mine Roadway Model Based on Point Cloud Feature Matching

Yang

Luo

Zhang

et al. 2022

Mathematical Problems in Engineering

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A coal mine roadway is a longitudinally limited space with curves and branches, low illumination and high humidity, a large amount of dust, and an unstructured terrain environment. Traditional ICP algorithms have the defects of slow convergence speed and it is easy to fall into local optimums. While the NDT algorithm in the NDT + ICP algorithm has high registration efficiency, poor stability, and low registration accuracy, which are not suitable for point clouds with noise and a large amount of data. By calculating the FPFH value, the detailed description of the point cloud will be greatly increased to increase the robustness and accuracy Therefore, a feature registration method based on the FPFH + ICP algorithm is proposed to reduce the modeling error of excavation roadways and meet the requirements of intelligent excavation. First, outliers caused by dust are treated by the Euclidean clustering point cloud segmentation method, and then the calculation of the normal vector in the FPFH feature descriptor is optimized based on extracting key points from the roadway structure. The surface normal vector of each key point and its neighborhood point is estimated according to the measured point and its neighborhood point. The initial coordinate transformation matrix of a point cloud of an excavated roadway is obtained by the SAC-IA algorithm and transferred to the ICP algorithm. Finally, KD-tree is introduced into the ICP algorithm to accelerate the search speed of corresponding point pairs, and the Gauss–Newton method is used to optimize the solution of the nonlinear objective function of the algorithm to complete accurate registration of point clouds in an excavation roadway.

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“…The presented method also contributed to improving accuracy by replacing single-Probability Density Functions (PDF) with mixed PDFs. The papers of [ 23 , 24 ] introduced an NDT-ICP algorithm that combines and operates NDT and ICP sequentially. The suggested method divides the registration process into two stages and performs a coarse registration with NDT.…”

Section: Related Workmentioning

confidence: 99%

Integrated Pose Estimation Using 2D Lidar and INS Based on Hybrid Scan Matching

Park

Lee

Sung

2021

Sensors

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Point cloud data is essential measurement information that has facilitated an extended functionality horizon for urban mobility. While 3D lidar and image-depth sensors are superior in implementing mapping and localization, sense and avoidance, and cognitive exploration in an unknown area, applying 2D lidar is inevitable for systems with limited resources of weight and computational power, for instance, in an aerial mobility system. In this paper, we propose a new pose estimation scheme that reflects the characteristics of extracted feature point information from 2D lidar on the NDT framework for exploiting an improved point cloud registration. In the case of the 2D lidar point cloud, vertices and corners can be viewed as representative feature points. Based on this feature point information, a point-to-point relationship is functionalized and reflected on a voxelized map matching process to deploy more efficient and promising matching performance. In order to present the navigation performance of the mobile object to which the proposed algorithm is applied, the matching result is combined with the inertial navigation through an integration filter. Then, the proposed algorithm was verified through a simulation study using a high-fidelity flight simulator and an indoor experiment. For performance validation, both results were compared and analyzed with the previous techniques. In conclusion, it was demonstrated that improved accuracy and computational efficiency could be achieved through the proposed algorithms.

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Research on Point Cloud Registering Method of Tunneling Roadway Based on 3D NDT-ICP Algorithm

Cited by 21 publications

References 18 publications

Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios

Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios

Establishment of a Coal Mine Roadway Model Based on Point Cloud Feature Matching

Integrated Pose Estimation Using 2D Lidar and INS Based on Hybrid Scan Matching

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