한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제38권5호
  • /
  • Pages.425-433
  • /
  • 2020
  • /
  • 1598-4850(pISSN)
  • /
  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
권호 표지
DOI QR코드

DOI QR Code

Improved Georeferencing of a Wearable Indoor Mapping System Using NDT and Sensor Integration

  • Do, Linh Giang (Dept. of Civil and Environmental Engineering, Myongji University) ;
  • Kim, Changjae (Dept. of Civil and Environmental Engineering, Myongji University) ;
  • Kim, Han Sae (Dept. of Civil and Environmental Engineering, Myongji University)
  • 투고 : 2020.09.15
  • 심사 : 2020.10.21
  • 발행 : 2020.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Three-dimensional data has been used for different applications such as robotics, building reconstruction, and so on. 3D data can be generated from an optical camera or a laser scanner. Especially, a wearable multi-sensor system including the above-mentioned sensors is an optimized structure that can overcome the drawbacks of each sensor. After finding the geometric relationships between sensors, georeferencing of the datasets acquired from the moving system, should be carried out. Especially, in an indoor environment, error propagation always causes problem in the georeferencing process. To improve the accuracy of this process, other sources of data were used to combine with LiDAR (Light Detection and Ranging) data, and various registration methods were also tested to find the most suitable way. More specifically, this paper proposed a new process of NDT (Normal Distribution Transform) to register the LiDAR point cloud, with additional information from other sensors. For real experiment, a wearable mapping system was used to acquire datasets in an indoor environment. The results showed that applying the new process of NDT and combining LiDAR data with IMU (Inertial Measurement Unit) information achieved the best result with the RMSE 0.063 m.

키워드

참고문헌

  1. Besl, P.J., and McKay, N.D. (1992), A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 2, pp. 239-256. https://doi.org/10.1109/34.121791
  2. Biber, P. (2003), The Normal Distributions Transform: A new approach to laser scan matching, IEEE International Conference on Intelligent Robots and Systems, 27-31 October, Las Vegas, USA, Vol. 3, pp. 2743-2748.
  3. Chen, Y., Chen, R., Chen, X., Chen, W., and Wang, Q. (2011), Wearable electromyography sensor based outdoorindoor seamless pedestrian navigation using motion recognition method, 2011 International Conference on Indoor Positioning and Indoor Navigation, IEEE, 21-23 September, Guimaraes, Portugal, pp. 1-9.
  4. Choi, K.H. (2018), Multi-Sensor System Calibration for Lidar and Fish-eye Lens Camera Data Integration, Ph.D Dissertation, Seoul National University, Seoul, Korea, 174p.
  5. Deilamsalehy, H., and Havens, T. C. (2017), Sensor fused three-dimensional localization using IMU, camera and LiDAR, 2016 IEEE Sensors, IEEE, 30 October-3 November, Orlando, USA, pp. 1-3.
  6. Durrant-Whyte, H., and Bailey, T. (2006), Simultaneous localization and mapping: Part I, IEEE Robotics and Automation Magazine, IEEE, Vol. 13, No. 2, pp. 99-108. https://doi.org/10.1109/MRA.2006.1638022
  7. Fan, L., Smethurst, J. A., Atkinson, P. M., and Powrie, W. (2015), Error in target-based georeferencing and registration in terrestrial laser scanning, Computers and Geosciences, Vol. 83, pp. 54-64. https://doi.org/10.1016/j.cageo.2015.06.021
  8. Laguela, S., Dorado, I., Gesto, M., Arias, P., Gonzalez-Aguilera, D., and Lorenzo, H. (2018), Behavior analysis of novel wearable indoor mapping system based on 3d-slam, Sensors, Vol. 18, No. 3, pp. 1-16. https://doi.org/10.1109/JSEN.2017.2772700
  9. Magnusson, M., Lilienthal, A., and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT, Journal of Field Robotics, Vol. 24, Issue10, pp. 803-827. https://doi.org/10.1002/rob.20204
  10. Magnusson, M., Nuchter, A., Lorken, C., Lilienthal, A. J., and Hertzberg, J. (2009), Evaluation of 3D registration reliability and speed-a comparison of ICP and NDT, IEEE International Conference on Robotics and Automation, IEEE, 12-17 May, Kobe, Japan, pp. 3907-3912.
  11. Pang, S., Kent, D., Cai, X., Al-Qassab, H., Morris, D., and Radha, H. (2018), 3D scan registration based localization for autonomous vehicles-a comparison of NDT and ICP under realistic conditions, IEEE Vehicular Technology Conference, IEEE, 27-30 August, Chicago, USA, pp. 5-9.
  12. Peterson, L. (2009), K-nearest neighbor, Scholarpedia, http://scholarpedia.org/article/K-nearest_neighbor/ (last date accessed: 14 September 2020).
  13. Schuhmacher, S., and Bohm, J. (2005), Georeferencing of terrestrial laserscanner data for applications in architectural modelling, 3D-ARCH 2005: Virtual Reconstruction and Visualization of Complex Architectures, 22-24 August, Mestre-Venice, Italy, Vol. XXXVI, pp. 7.
  14. Vogel, S., Alkhatib, H., Bureick, J., Moftizadeh, R., and Neumann, I. (2019), Georeferencing of laser scanner-based kinematic multi-sensor systems in the context of iterated extended kalman filters using geometrical constraints, Sensors, Vol. 19, No. 10, pp. 2280. https://doi.org/10.3390/s19102280
  15. Wen, C., Pan, S., Wang, C., and Li, J. (2016), An indoor backpack system for 2-D and 3-D mapping of building interiors, IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 7, pp. 992-996. https://doi.org/10.1109/LGRS.2016.2558486
  16. Zhang, J., and Singh, S. (2014), LOAM: Lidar odometry and mapping in real-time, Robotics: Science and Systems, 13-15 July, Rome, Italy, Vol. 2, No. 9.
  17. Zhang, J., and Singh, S. (2015), Visual-lidar odometry and mapping: low-drift, robust, and fast, IEEE International Conference on Robotics and Automation, IEEE, 26-30 May, Seattle, USA, pp. 2174-2181.

피인용 문헌

  1. Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles vol.9, pp.12, 2020, https://doi.org/10.3390/electronics9122084