Abstract
We propose a real-time, low-drift laser odometry approach that tightly integrates sequentially measured 3D multi-beam LIDAR data with inertial measurements. The laser measurements are motion-compensated using a novel algorithm based on non-rigid registration of two consecutive laser sweeps and a local map. IMU data is being tightly integrated by means of factor-graph optimization on a pose graph. We evaluate our method on a public dataset and also obtain results on our own datasets that contain information not commonly found in existing datasets. At the time of writing, our method was ranked within the top five laser-only algorithms of the KITTI odometry benchmark.
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Notes
- 1.
Our implementation is also able to close loops, but to maintain focus of this work, we decided to focus on the odometry part of the SLAM problem, whose accuracy is essential to obtain accurate maps—even when loops are being closed.
- 2.
At the time of writing, the best method using only laser data was the LOAM method by Zhang and Singh. However, the results reported on the KITTI benchmark webpage are no longer equivalent to those reported in their paper [22], indicating that their solution has been updated. The updated algorithm is no longer publicly available.
- 3.
This is relatively efficient, because it is done for the set of query points and not for the whole point cloud.
- 4.
Note that we use a log map of \(S^3\) to \(\mathbb {R}^3\) for measuring the ‘difference’ between the orientations [11].
- 5.
http://www.cvlibs.net/datasets/kitti/eval_odometry.php see entry ‘MC2SLAM’.
- 6.
- 7.
This number was manually determined to be sufficient to reduce the deviation of the results to negligible values.
References
Agarwal, S., Mierle, K., et al.: Ceres solver. http://ceres-solver.org
Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–607. International Society for Optics and Photonics (1992)
Board, I.: IEEE standard specification format guide and test procedure for single-axis interferometric fiber optic gyros. IEEE Std., pp. 952–1997 (1998)
Bosse, M., Zlot, R., Flick, P.: Zebedee: design of a spring-mounted 3-D range sensor with application to mobile mapping. IEEE Trans. Robot. 28(5), 1104–1119 (2012)
Cvišić, I., Petrović, I.: Stereo odometry based on careful feature selection and tracking. In: 2015 European Conference on Mobile Robots, ECMR, pp. 1–6. IEEE (2015)
Deschaud, J.: IMLS-SLAM: scan-to-model matching based on 3D data. CoRR abs/1802.08633 (2018). http://arxiv.org/abs/1802.08633
Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In: Proceedings of Robotics: Science and Systems, Rome, Italy, July 2015. https://doi.org/10.15607/RSS.2015.XI.006
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition, CVPR (2012)
Hertzberg, C., Wagner, R., Frese, U., Schröder, L.: Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds. Inf. Fusion 14(1), 57–77 (2013)
Hesch, J.A., Kottas, D.G., Bowman, S.L., Roumeliotis, S.I.: Consistency analysis and improvement of vision-aided inertial navigation. IEEE Trans. Robot. 30(1), 158–176 (2014)
Kaess, M.: Simultaneous localization and mapping with infinite planes. In: ICRA, vol. 1, p. 2 (2015)
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: Incremental smoothing and mapping using the Bayes tree. Int. J. Robot. Res. 31(2), 216–235 (2012)
Kukko, A., Kaartinen, H., Hyyppä, J., Chen, Y.: Multiplatform mobile laser scanning: usability and performance. Sensors 12(9), 11712–11733 (2012)
Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual–inertial odometry using nonlinear optimization. Int. J. Robot. Res. 34(3), 314–334 (2015)
Low, K.L.: Linear least-squares optimization for point-to-plane ICP surface registration, no. 4. Chapel Hill, University of North Carolina (2004)
Moosmann, F., Stiller, C.: Velodyne SLAM. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 393–398, Baden-Baden, Germany, June 2011
Nüchter, A., Bleier, M., Schauer, J., Janotta, P.: Improving Google’s cartographer 3D mapping by continuous-time SLAM. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42, 543 (2017)
Nüchter, A., Borrmann, D., Koch, P., Kühn, M., May, S.: A man-portable, IMU-free mobile mapping system. ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. 2, 17–23 (2015). https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/17/2015/
Rönnholm, P., Liang, X., Kukko, A., Jaakkola, A., Hyyppä, J.: Quality analysis and correction of mobile backpack laser scanning data. ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. 3, 41 (2016)
Zhang, J., Kaess, M., Singh, S.: Real-time depth enhanced monocular odometry. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, pp. 4973–4980. IEEE (2014)
Zhang, J., Kaess, M., Singh, S.: A real-time method for depth enhanced visual odometry. Auton. Robots 41(1), 31–43 (2017)
Zhang, J., Singh, S.: LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems, vol. 2 (2014)
Zhang, J., Singh, S.: Visual-lidar odometry and mapping: low-drift, robust, and fast. In: 2015 IEEE International Conference on Robotics and Automation, ICRA, pp. 2174–2181. IEEE (2015)
Zhang, J., Singh, S.: Low-drift and real-time lidar odometry and mapping. Auton. Robots 41(2), 401–416 (2017)
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The authors would like to thank three anonymous reviewers for their helpful comments.
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Neuhaus, F., Koß, T., Kohnen, R., Paulus, D. (2019). MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_5
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