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An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair

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Abstract

Light Detection And Ranging (LiDAR) has been widely employed in Unmanned Ground Vehicle (UGV) for autonomous navigation and object detection. In this paper, an efficient extrinsic parameter calibration approach, which is based on a pair of orthogonal normal vectors, is presented for an arbitrary equipped 3-D LiDAR. With the proposed approach, the whole calibration process can be easily and efficiently implemented in outdoor urban environment and no calibration equipment is required. The main advantages of this approach are twofold: (1) compared with traditional ways, the proposed approach employs an orthogonal normal vector pair, which is generated by ground plane and vertical wall in urban environment, so calibration equipments are not required anymore; (2) the normal vector is estimated from the point cloud data on a surface, thus a quite robust and accuracy estimation can be obtained. Experiments illustrate the effective and efficient performance of the proposed approach, compared with the state of the art.

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References

  1. Zhou, S., Xi, J., McDaniel, M.W., Nishihata, T., Salesses, P., Iagnemma, K.: Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain. J. Field Robot. 29 (2), 277–297 (2012)

    Article  Google Scholar 

  2. Sohn, H.J., Kim, B.K.: An efficient localization algorithm based on vector matching for mobile robots using laser range finders. J. Intell. Robot. Syst. 51 (4), 461–488 (2008)

    Article  Google Scholar 

  3. Lin, H.-H., Tsai, C.-C.: Laser pose estimation and tracking using fuzzy extended information filtering for an autonomous mobile robot. J. Intell. Robot. Syst. 53 (2), 119–143 (2008)

    Article  Google Scholar 

  4. Bouguet, J.-Y.: Camera calibration toolbox for matlab (2004)

  5. Muhammad, N., Lacroix, S.: Calibration of a rotating multi-beam lidar. In: International Conference on Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ. IEEE, pp. 5648–5653 (2010)

  6. Atanacio-Jiménez, G., González-Barbosa, J.-J., Hurtado-Ramos, J.B., Ornelas-Rodríguez, F.J., Jiménez-Hernández, H., García-Ramirez, T., González-Barbosa, R.: Lidar velodyne hdl-64e calibration using pattern planes. Int. J. Adv. Robot. Syst. 8 (5), 70–82 (2011)

    Google Scholar 

  7. Glennie, C., Lichti, D.D.: Static calibration and analysis of the velodyne hdl-64e s2 for high accuracy mobile scanning. Remote Sens. 2 (6), 1610–1624 (2010)

    Article  Google Scholar 

  8. Kwak, K., Huber, D.F., Badino, H., Kanade, T.: Extrinsic calibration of a single line scanning lidar and a camera. In: International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ. IEEE, pp. 3283-3289 (2011)

  9. Schneider, S., Himmelsbach, M., Luettel, T., Wuensche, H-J: Fusing vision and lidar-synchronization, correction and occlusion reasoning. In: Intelligent Vehicles Symposium (IV), 2010 IEEE. IEEE, pp. 388–393 (2010)

  10. Scaramuzza, D., Harati, A., Siegwart, R.: Extrinsic self calibration of a camera and a 3d laser range finder from natural scenes. In: International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ. IEEE, pp. 4164–4169 (2007)

  11. Unnikrishnan, R., Hebert, M.: Fast extrinsic calibration of a laser rangefinder to a camera (2005)

  12. Li, G., Liu, Y., Dong, L., Cai, X., Zhou, D.: An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features. In: International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ. IEEE, pp. 3854–3859 (2007)

  13. Cobzas, D., Zhang, H., Jagersand, M.: A comparative analysis of geometric and image-based volumetric and intensity data registration algorithms. In: International Conference on Robotics and Automation (ICRA). Proceedings. IEEE, vol. 3, pp. 2506–2511 (2002)

  14. Gao, C., Spletzer, J.R.: On-line calibration of multiple lidars on a mobile vehicle platform. In: International Conference on Robotics and Automation (ICRA). IEEE pp. 279–284 (2010)

  15. Underwood, J., Hill, A., Scheding, S.: Calibration of range sensor pose on mobile platforms. In: International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ. IEEE, pp. 3866–3871 (2007)

  16. Geiger, A., Moosmann, F., Car, O., Schuster, B.: Automatic camera and range sensor calibration using a single shot. In: International Conference on Robotics and Automation (ICRA). IEEE, pp. 3936–3943 (2012)

  17. Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-d point sets. In IEEE Transactions on Pattern Analysis and Machine Intelligence. no. 5, pp. 698–700 (1987)

  18. Titterton, D.: Strapdown inertial navigation technology. IET, vol 17 (2004)

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Correspondence to Erke Shang.

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Shang, E., An, X., Shi, M. et al. An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair. J Intell Robot Syst 79, 21–36 (2015). https://doi.org/10.1007/s10846-014-0080-3

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  • DOI: https://doi.org/10.1007/s10846-014-0080-3

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