Skip to main content
SpringerLink
Log in

Low-drift and real-time lidar odometry and mapping

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Here we propose a real-time method for low-drift odometry and mapping using range measurements from a 3D laser scanner moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation (especially without an external reference such as GPS) cause mis-registration of the resulting point cloud. To date, coherent 3D maps have been built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift in motion estimation and low-computational complexity. The key idea that makes this level of performance possible is the division of the complex problem of Simultaneous Localization and Mapping, which seeks to optimize a large number of variables simultaneously, into two algorithms. One algorithm performs odometry at a high-frequency but at low fidelity to estimate velocity of the laser scanner. Although not necessary, if an IMU is available, it can provide a motion prior and mitigate for gross, high-frequency motion. A second algorithm runs at an order of magnitude lower frequency for fine matching and registration of the point cloud. Combination of the two algorithms allows map creation in real-time. Our method has been evaluated by indoor and outdoor experiments as well as the KITTI odometry benchmark. The results indicate that the proposed method can achieve accuracy comparable to the state of the art offline, batch methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Notes

  1. www.cvlibs.net/datasets/kitti/eval_odometry.php.

References

  • Andersen, R. (2008). Modern methods for robust regression. Sage University Paper Series on Quantitative Applications in the Social Sciences.

  • Anderson, S. & Barfoot, T. (2013). Towards relative continuous-time SLAM. In IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.

  • Anderson, S., & Barfoot, T. (2013). RANSAC for motion-distorted 3D visual sensors. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan.

  • Badino, H., & Kanade, T. (2011). A head-wearable short-baseline stereo system for the simultaneous estimation of structure and motion. In IAPR Conference on Machine Vision Application, Nara, Japan.

  • Badino, A.Y.H., & Kanade, T. (2013). Visual odometry by multi-frame feature integration. In Workshop on Computer Vision for Autonomous Driving (Collocated with ICCV 2013). Sydney, Australia.

  • Bay, H., Ess, A., Tuytelaars, T., & Gool, L. (2008). SURF: Speeded up robust features. Computer Vision and Image Understanding, 110(3), 346–359.

    Article  Google Scholar 

  • Bellavia, F., Fanfani, M., Pazzaglia, F., & Colombo, C. (2013). Robust selective stereo slam without loop closure and bundle adjustment. Lecture Notes in Computer Science, 8156, 462–471.

    Article  Google Scholar 

  • Bosse, M., & and Zlot, R. (2009). Continuous 3D scan-matching with a spinning 2D laser. In IEEE International Conference on Robotics and Automation, Kobe, Japan.

  • Bosse, M., Zlot, R., & Flick, P. (2012). Zebedee: Design of a spring-mounted 3-D range sensor with application to mobile mapping. IEEE Transactions on Robotics, 28(5), 1104–1119.

    Article  Google Scholar 

  • de Berg, M., van Kreveld, M., Overmars, M., & Schwarzkopf, O. (2008). Computation geometry: Algorithms and applications (3rd ed.). Berlin: Springer.

    Book  MATH  Google Scholar 

  • Dong, H. & Barfoot, T. (2012). Lighting-invariant visual odometry using lidar intensity imagery and pose interpolation. In The 7th International Conference on Field and Service Robots, Matsushima, Japan.

  • Furgale, P., Barfoot, T. & Sibley, G. (2012). Continuous-time batch estimation using temporal basis functions. In IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN.

  • Geiger, A., Lenz, P. & Urtasun, R. (2012). Are we ready for autonomous driving? The kitti vision benchmark suite. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3354–3361).

  • Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. International Journal of Robotics Research, 32, 1229–1235.

    Article  Google Scholar 

  • Guo, C.X., Kottas, D.G., DuToit, R.C., Ahmed, A., Li, R. & Roumeliotis, S.I. (2014). Efficient visual-inertial navigation using a rolling-shutter camera with inaccurate timestamps. In Proceedings of Robotics: Science and Systems, Berkeley, CA.

  • Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision. New York: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Hong, S., Ko, H. & Kim, J. (2010). VICP: Velocity updating iterative closest point algorithm. In IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska.

  • Li, Y. & Olson, E. (2011) Structure tensors for general purpose LIDAR feature extraction. In IEEE International Conference on Robotics and Automation, Shanghai, China, May 9–13.

  • Li, M., & Mourikis, A. (2014). Vision-aided inertial navigation with rolling-shutter cameras. International Journal of Robotics Research, 33(11), 1490–1507.

    Article  Google Scholar 

  • Lu, W., Xiang, Z., & Liu, J. (2013). High-performance visual odometry with two-stage local binocular ba and gpu. In IEEE Intelligent Vehicles Symposium. Gold Coast City, Australia.

  • Moosmann, F., & Stiller, C. (2011). Velodyne SLAM. In IEEE Intelligent Vehicles Symposium (IV). Baden-Baden, Germany.

  • Murray, R., & Sastry, S. (1994). A mathematical introduction to robotic manipulaton. Boca Raton: CRC Press.

    Google Scholar 

  • Nuchter, A., Lingemann, K., Hertzberg, J., & Surmann, H. (2007). 6D SLAM-3D mapping outdoor environments. Journal of Field Robotics, 24(8–9), 699–722.

    Article  MATH  Google Scholar 

  • Persson, M., Piccini, T., Mester, R., & Felsberg, M. (2015). Robust stereo visual odometry from monocular techniques. In IEEE Intelligent Vehicles Symposium. Seoul, Korea.

  • Pomerleau, F., Colas, F., Siegwart, R., & Magnenat, S. (2013). Comparing ICP variants on real-world data sets. Autonomous Robots, 34(3), 133–148.

    Article  Google Scholar 

  • Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., et al. (2009). ROS: An open-source robot operating system. In Workshop on Open Source Software (Collocated with ICRA 2009). Kobe, Japan.

  • Rosen, D., Huang, G., & Leonard, J. (2014). Inference over heterogeneous finite-/infinite-dimensional systems using factor graphs and Gaussian processes. In IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.

  • Rusinkiewicz, S. & Levoy, M. (2001). Efficient variants of the ICP algorithm. In Third International Conference on 3D Digital Imaging and Modeling (3DIM), Quebec City, Canada.

  • Rusu, R.B., & Cousins, S. (2011). 3D is here: Point Cloud Library (PCL). In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9–13.

  • Scherer, S., Rehder, J., Achar, S., Cover, H., Chambers, A., Nuske, S., et al. (2012). River mapping from a flying robot: State estimation, river detection, and obstacle mapping. Autonomous Robots, 32(5), 1–26.

    Google Scholar 

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge, MA: The MIT Press.

    MATH  Google Scholar 

  • Tong, C. H. & Barfoot, T. (2013). Gaussian process Gauss-Newton for 3D laser-based visual odometry. In IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.

  • Tong, C., Furgale, P., & Barfoot, T. (2013). Gaussian process Gauss-newton for non-parametric simultaneous localization and mapping. International Journal of Robotics Research, 32(5), 507–525.

    Article  Google Scholar 

  • Zhang, J. & Singh, S. (2014). LOAM: Lidar odometry and mapping in real-time. In Robotics: Science and Systems Conference (RSS), Berkeley, CA.

  • Zhang, J. & Singh, S. (2015). Visual-lidar odometry and mapping: Low-drift, robust, and fast. In IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, May

  • Zlot, R. & Bosse, M. (2012). Efficient large-scale 3D mobile mapping and surface reconstruction of an underground mine. In The 7th International Conference on Field and Service Robots, Matsushima, Japan.

Download references

Acknowledgments

The paper is based upon work supported by the National Science Foundation under Grant No. IIS-1328930. Special thanks are given to D. Huber, S. Scherer, M. Bergerman, M. Kaess, L. Yoder, S. Maeta for their insightful inputs and invaluable help.

Author information

Authors and Affiliations

  1. Robotics Institute at Carnegie Mellon University, Pittsburgh, USA

    Ji Zhang & Sanjiv Singh

Authors
  1. Ji Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjiv Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ji Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Singh, S. Low-drift and real-time lidar odometry and mapping. Auton Robot 41, 401–416 (2017). https://doi.org/10.1007/s10514-016-9548-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-016-9548-2

Keywords

Search

Navigation