3D Environment Modelling of Mobile Robot based on Virtual Reality and Point Cloud Technology

被引:0
作者
Wang Q. [1 ]
Xiong A. [1 ]
Zhu H. [2 ]
机构
[1] Chongqing University of Posts and Telecommunications, Nan An, Chongqing
[2] ChongQing Jiao Tong University, Nan An, Chongqing
来源
Computer-Aided Design and Applications | 2023年 / 20卷 / S14期
关键词
3D modeling; indoor modeling; virtual reality; visual simultaneous localization and map building;
D O I
10.14733/cadaps.2023.S14.216-230
中图分类号
学科分类号
摘要
Traditional virtual reality (VR) technology generates indoor 3D map models through human modeling, which has problems such as slow response time and bias. For this reason, we propose a robot-aware environment modeling scheme with altered point cloud generation algorithm and VR. First, the 3D point cloud is reconstructed into a robot-sensitive environment model by environment-aware reconstruction algorithm and imported into the computational architecture. Then, the robot is repositioned in different environments by visual localization techniques and the robot pose is mapped in real time to achieve human-robot interaction. Experiments show that using visual repositioning techniques with point clouds to build environment-aware models is not only fast, but also solves the scene scale deviation and enables the reuse of maps. At the same time, the VR technology enables the operator to obtain a strong sense of immersion. © 2023 CAD Solutions, LLC.
引用
收藏
页码:216 / 230
页数:14
相关论文
共 25 条
  • [1] Atiyah H. A., Hassan M. Y., Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm, Engineering and Technology Journal, 39, pp. 965-976, (2021)
  • [2] Bavelos A. C., Kousi N., Gkournelos C., Lotsaris K., Aivaliotis S., Michalos G., Makris S., Enabling flexibility in manufacturing by integrating shopfloor and process perception for mobile robot workers, Applied Sciences, 11, 9, (2021)
  • [3] Cui X., Lu C., Wang J., 3D semantic map construction using improved ORB-SLAM2 for mobile robot in edge computing environment, IEEE Access, 8, pp. 67179-67191, (2020)
  • [4] Han L., Zheng T., Zhu Y., Xu L., Fang L., Live semantic 3d perception for immersive augmented reality, IEEE transactions on visualization and computer graphics, 26, 5, pp. 2012-2022, (2020)
  • [5] Kim P., Park J., Cho Y. K., Kang J., UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments, Automation in Construction, 106, (2019)
  • [6] Kolhatkar C., Wagle K., Review of SLAM algorithms for indoor mobile robot with LIDAR and RGB-D camera technology, Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE 2020, 2021, pp. 397-409
  • [7] Li X., Du S., Li G., Li H., Integrate point-cloud segmentation with 3D LiDAR scan-matching for mobile robot localization and mapping, Sensors, 20, 1, (2019)
  • [8] Linxi G. O. N. G., Yunfei C. A. I., Human Following for Outdoor Mobile Robots Based on Point‐ Cloud's Appearance Model, Chinese Journal of Electronics, 30, 6, pp. 1087-1095, (2021)
  • [9] Liu H., Song R., Zhang X., Liu H., Point cloud segmentation based on Euclidean clustering and multi-plane extraction in rugged field, Measurement Science and Technology, 32, 9, (2021)
  • [10] Lv Z., Lloret J., Song H., Real-time image processing for augmented reality on mobile devices, Journal of Real-Time Image Processing, 18, pp. 245-248, (2021)