[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Semi-dense visual-inertial odometry and mapping for computationally constrained platforms

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

In this paper we present a direct semi-dense stereo Visual-Inertial Odometry (VIO) algorithm enabling autonomous flight for quadrotor systems with Size, Weight, and Power (SWaP) constraints. The proposed approach is validated through experiments on a 250 g, 22 cm diameter quadrotor equipped with a stereo camera and an IMU. Semi-dense methods have superior performance in low texture areas, which are often encountered in robotic tasks such as infrastructure inspection. However, due to the measurement size and iterative nonlinear optimization, these methods are computationally more expensive. As the scale of the platform shrinks down, the available computation of the on-board CPU becomes limited, making autonomous navigation using optimization-based semi-dense tracking a hard problem. We show that our direct semi-dense VIO performs comparably to other state-of-the-art methods, while taking less CPU than other optimization-based approaches, making it suitable for computationally-constrained small platforms. Our method takes less amount of CPU than the state-of-the-art semi-dense method, VI-Stereo-DSO, due to a simpler framework in the algorithm and a multi-threaded code structure allowing us to run real-time state estimation on an ARM board. With a low texture dataset obtained with our quadrotor platform, we show that this method performs significantly better than sparse methods in low texture conditions encountered in indoor navigation. Finally, we demonstrate autonomous flight on a small platform using our direct semi-dense Visual-Inertial Odometry. Supplementary code, low texture datasets and videos can be found on our github repo: https://github.com/KumarRobotics/sdd_vio.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://github.com/MichaelGrupp/evo.

  2. We are using a different CPU for runtime analysis from the previous experiments on datasets, for the two sets of experiments are conducted at different times.

References

  • Baker, S., & Matthews, I. (2004). Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision, 56(3), 221–255.

    Article  Google Scholar 

  • Bloesch, M., Burri, M., Omari, S., Hutter, M., & Siegwart, R. (2017). Iterated extended kalman filter based visual-inertial odometry using direct photometric feedback. The International Journal of Robotics Research, 36(10), 1053–1072.

    Article  Google Scholar 

  • Chirikjian, G. S. (2011). Stochastic models, information theory, and lie groups, volume 2: Analytic methods and modern applications. Berlin: Springer Science Business Media.

    Google Scholar 

  • Engel, J., Sturm, J., & Cremers, D. (2013). Semi-dense visual odometry for a monocular camera. In: Proceedings of the IEEE international conference on computer vision, pp. 1449–1456.

  • Engel, J., Stückler, J., & Cremers, D. (2015). Large-scale direct slam with stereo cameras. In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, IEEE, pp. 1935–1942.

  • Engel, J., Usenko, V., & Cremers, D. (2016). A photometrically calibrated benchmark for monocular visual odometry. arXiv preprint arXiv:1607.02555

  • Engel, J., Koltun, V., & Cremers, D. (2018). Direct sparse odometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3), 611–625.

    Article  Google Scholar 

  • Forster, C., Carlone, L., Dellaert, F., & Scaramuzza, D. (2017a). On-manifold preintegration for real-time visual-inertial odometry. IEEE Transactions on Robotics, 33(1), 1–21.

    Article  Google Scholar 

  • Forster, C., Zhang, Z., Gassner, M., Werlberger, M., & Scaramuzza, D. (2017b). Svo: Semidirect visual odometry for monocular and multicamera systems. IEEE Transactions on Robotics, 33(2), 249–265.

    Article  Google Scholar 

  • Fraundorfer, F., Heng, L., Honegger, D., Lee, GH., Meier, L., Tanskanen, P., & Pollefeys, M. (2012). Vision-based autonomous mapping and exploration using a quadrotor mav. In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE, pp. 4557–4564.

  • Geneva, P., Eckenhoff, K., Lee, W., Yang, & Y., Huang, G. (2020). Openvins: A research platform for visual-inertial estimation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 4666–4672.

  • Gui, J., Gu, D., Wang, S., & Hu, H. (2015). A review of visual inertial odometry from filtering and optimisation perspectives. Advanced Robotics, 29(20), 1289–1301.

    Article  Google Scholar 

  • Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • Hérissé, B., Hamel, T., Mahony, R., & Russotto, F. X. (2012). Landing a VTOL Unmanned Aerial Vehicle on a moving platform using optical flow. IEEE Transactions on Robotics, 28(1), 77–89.

    Article  Google Scholar 

  • Irani, M., & Anandan, P. (1999). About direct methods. In: International Workshop on Vision Algorithms, Springer, pp. 267–277.

  • Klein, G., & Murray, D. (2009). Parallel tracking and mapping on a camera phone. International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 83–86). USA: Orlando.

    Google Scholar 

  • Lee, T., Leok, M., & McClamroch, NH. (2010). Geometric tracking control of a quadrotor uav on se(3). In: 49th IEEE Conference on Decision and Control (CDC), pp. 5420–5425.

  • Liu, W., Loianno, G., Mohta, K., Daniilidis, K., & Kumar, V. (2018). Semi-dense visual-inertial odometry and mapping for quadrotors with swap constraints. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 1–6.

  • Ma, Y., Soatto, S., Kosecka, J., & Sastry, S. S. (2012). An invitation to 3-d vision: From images to geometric models. Berlin: Springer Science & Business Media.

    MATH  Google Scholar 

  • Meier, L., Tanskanen, P., Fraundorfer, F., & Pollefeys, M. (2011). Pixhawk: A system for autonomous flight using onboard computer vision. In: 2011 IEEE International Conference on Robotics and Automation, IEEE, pp. 2992–2997.

  • Michael, N., Mellinger, D., Lindsey, Q., & Kumar, V. (2010). The grasp multiple micro-UAV test bed. IEEE Robotics and Automation Magazine, 17(3), 56–65.

    Article  Google Scholar 

  • Mourikis, A. I., & Roumeliotis, S. I. (2007). (2007) A multi-state constraint kalman filter for vision-aided inertial navigation. Robotics and automation (pp. 3565–3572). IEEE: IEEE international conference on.

  • Mur-Artal, R., & Tardós, J. D. (2017). Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Transactions on Robotics, 33(5), 1255–1262.

    Article  Google Scholar 

  • Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011). Dtam: Dense tracking and mapping in real-time. In: 2011 international conference on computer vision, IEEE, pp. 2320–2327.

  • Ozaslan, T., Loianno, G., Keller, J., Taylor, C. J., Kumar, V., Wozencraft, J. M., & Hood, T. (2017). Autonomous navigation and mapping for inspection of penstocks and tunnels with mavs. IEEE Robotics and Automation Letters, 2(3), 1740–1747. https://doi.org/10.1109/LRA.2017.2699790.

    Article  Google Scholar 

  • Qin, T., Li, P., & Shen, S. (2018). Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 34(4), 1004–1020.

    Article  Google Scholar 

  • Schöps, T., Engel, J., & Cremers, D. (2014). (2014) Semi-dense visual odometry for ar on a smartphone. Mixed and Augmented Reality (ISMAR) (pp. 145–150). IEEE: IEEE International Symposium on.

  • Shen, S., Mulgaonkar, Y., Michael, N., & Kumar, V. (2014). Multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft mav. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 4974–4981.

  • Sun, K., Mohta, K., Pfrommer, B., Watterson, M., Liu, S., Mulgaonkar, Y., et al. (2018). Robust stereo visual inertial odometry for fast autonomous flight. IEEE Robotics and Automation Letters, 3(2), 965–972.

    Article  Google Scholar 

  • Thakur, D., Loianno, G., Liu, W., & Kumar, V. (2018). Nuclear environments inspection with micro aerial vehicles: Algorithms and experiments. In: International Symposium on Experimental Robotics, Springer, pp. 191–200.

  • Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., et al. (2012). Toward a fully autonomous uav: Research platform for indoor and outdoor urban search and rescue. IEEE Robotics Automation Magazine, 19(3), 46–56. https://doi.org/10.1109/MRA.2012.2206473.

    Article  Google Scholar 

  • Usenko, V., Engel, J., Stückler, J., & Cremers, D. (2016). Direct visual-inertial odometry with stereo cameras. In: Robotics and Automation (ICRA), 2016 IEEE International Conference on, IEEE, pp. 1885–1892.

  • Usenko, V., Demmel, N., Schubert, D., Stückler, J., & Cremers, D. (2019). Visual-inertial mapping with non-linear factor recovery. IEEE Robotics and Automation Letters, 5(2), 422–429.

    Article  Google Scholar 

  • Von Stumberg, L., Usenko, V., & Cremers, D. (2018). Direct sparse visual-inertial odometry using dynamic marginalization. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 2510–2517.

  • Weiss, S., Scaramuzza, D., & Siegwart, R. (2011). Monocular-SLAM-based navigation for autonomous micro helicopters in GPS denied environments. Journal of Field Robotics, 28(6), 854–874.

    Article  Google Scholar 

  • Weiss, S., Achtelik, MW., Lynen, S., Chli, M., & Siegwart, R. (2012). Real-time onboard visual-inertial state estimation and self-calibration of mavs in unknown environments. In: 2012 IEEE international conference on robotics and automation, IEEE, pp. 957–964.

  • Xu, W., Choi, D., & Wang, G. (2018). Direct visual-inertial odometry with semi-dense mapping. Computers & Electrical Engineering, 67, 761–775.

    Article  Google Scholar 

  • Zhang, Z., Sattler, T., & Scaramuzza, D. (2020). Reference pose generation for visual localization via learned features and view synthesis. arXiv preprint arXiv:2005.05179

Download references

Acknowledgements

We gratefully acknowledge the support of ONR grant N00014-20-1-2822, Qualcomm Research, United Technologies, the IoT4Ag Engineering Research Center funded by the National Science Foundation (NSF) under NSF Cooperative Agreement Number EEC-1941529, and C-BRIC, a Semiconductor Research Corporation Joint University Microelectronics Program, program cosponsored by DARPA.

Author information

Authors and Affiliations

  1. General Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania, Philadelphia, USA

    Wenxin Liu, Kartik Mohta, Kostas Daniilidis & Vijay Kumar

  2. Agile Robotics and Perception Lab, New York University, New York, USA

    Giuseppe Loianno

Authors
  1. Wenxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kartik Mohta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giuseppe Loianno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kostas Daniilidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vijay Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenxin Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, W., Mohta, K., Loianno, G. et al. Semi-dense visual-inertial odometry and mapping for computationally constrained platforms. Auton Robot 45, 773–787 (2021). https://doi.org/10.1007/s10514-021-10002-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-021-10002-z

Keywords

Search

Navigation