The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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LiDAR-based Mapping Considering Laser Reflectivity in Indoor Environments

실내 환경에서의 레이저 반사도를 고려한 라이다 기반 지도 작성

  • Roun Lee (Department of Robot Systems Engineering, Keimyung University) ;
  • Jeonghong Park (Advanced-Intelligent Ship Research Division, KRISO) ;
  • Seonghun Hong (Department of Robot Systems Engineering, Keimyung University)
  • Received : 2023.01.04
  • Accepted : 2023.03.06
  • Published : 2023.05.31
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Abstract

Light detection and ranging (LiDAR) sensors have been most widely used in terrestrial robotic applications because they can provide dense and precise measurements of the surrounding environments. However, the reliability of LiDAR measurements can considerably vary due to the different reflectivities of laser beams to the reflecting surface materials. This study presents a robust LiDAR-based mapping method for the varying laser reflectivities in indoor environments using the framework of simultaneous localization and mapping (SLAM). The proposed method can minimize the performance degradations in the SLAM accuracy by checking and discarding potentially unreliable LiDAR measurements in the SLAM front-end process. The gaps in point-cloud maps created by the proposed approach are filled by a Gaussian process regression method. Experimental results with a mobile robot platform in an indoor environment are presented to validate the effectiveness of the proposed methodology.

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References

  1. S.-W. Yang and C.-C. Wang, "On Solving Mirror Reflection in LIDAR Sensing," IEEE/ASME Transactions on Mechatronics, vol. 16, no. 2, pp. 255-265, Apr., 2011, DOI: 10.1109/TMECH.2010.2040113.
  2. X. Wang and J. Wang, "Detecting glass in simultaneous localisation and mapping," Robotics and Autonomous Systems, vol. 88, pp. 97-103, Feb., 2017, DOI: 10.1016/j.robot.2016.11.003.
  3. G. Grisetti, C. Stachniss, and W. Burgard, "Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling," IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, 2005, DOI: 10.1109/ROBOT.2005.1570477.
  4. H. Wang, C. Wang, and L. Xie, "Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment," IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1715-1721, Apr., 2021, DOI: 10.1109/LRA.2021.3059567.
  5. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, The MIT Press, 2005, DOI: 10.7551/mitpress/3206.001.0001.
  6. S. Hong and J. Kim, "Three-dimensional visual mapping of underwater ship hull surface using piecewise-planar SLAM," International Journal of Control, Automation and Systems, vol. 18, no. 3, pp. 564-574, 2020, DOI: 10.1007/s12555-019-0646-8.
  7. W. Hess, D. Kohler, H. Rapp, and D. Andor, "Real-time loop closure in 2D LIDAR SLAM," IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016, DOI: 10.1109/ICRA.2016.7487258.
  8. M. Roy, S. Foufou, and F. Truchetet, "Mesh comparison using attribute deviation metric," International Journal of Image and Graphics, vol. 4, no. 1, pp. 127-140, 2004, DOI: 10.1142/S0219467804001324.