首页|Improved 3D laser point cloud reconstruction for autonomous mobile robot applications by using SVM-R technique

Improved 3D laser point cloud reconstruction for autonomous mobile robot applications by using SVM-R technique

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  • NETL
  • NSTL
  • Emerald Group Publishing
© 2024, Emerald Publishing Limited.Purpose: In autonomous mobile robots, high-level accuracy and precision in 3D perception are required for object detection, shape estimation and obstacle distance measurement. However, the existing methods suffer from limitations like inaccurate point clouds, noise in sensor data and synchronization problems between 2D LiDAR and servomotor. These factors can lead to the wrong perception and also introduce noise during sensor registration. Thus, the purpose of this study is to address these limitations and enhance the perception in autonomous mobile robots. Design/methodology/approach: A new sensor mounting structure is developed for 3D mapping by using a 2D LiDAR and servomotor. The proposed method uses a support vector machine regression (SVM-R) technique to optimize the waypoints of the servomotor for the point cloud reconstruction process and to obtain a highly accurate and detailed representation of the environment. Findings: The study includes an analysis of the SVM-R model, including Linear, radial basis function (RBF) and Polynomial kernel. Results show that the Linear kernel performs better with the lowest 3.67 mean absolute error (MAE), 26.24 mean squared error (MSE) and 5.12 root mean squared error (RMSE) values than the RBF and Polynomial kernels. The 2D to 3D point cloud reconstruction shows that the proposed method with a new sensor mounting structure performs better in perception accuracy and achieves an error of 0.45% in measuring the height of the target objects whereas in previous techniques the error was very large. Originality/value: The study shows the effectiveness of SVM-R in the 3D point cloud reconstruction process and exhibits remarkable performance for object height measurement. Further, the proposed technique is applicable for future advanced visual applications and has a superior performance over the other conventional methods.

Autonomous mobile robot (AMR)Environmental perceptionPoint cloud reconstructionSupport vector machine regression (SVM-R)

Singh M.、Nagla K.S.

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Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar

Dr B R Ambedkar National Institute of Technology Jalandhar

2024

10.1108/IJIUS-08-2024-0235

International journal of intelligent unmanned systems

International journal of intelligent unmanned systems

ISSN：2049-6427
年,卷(期)：2024.12(4)
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