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x-RIO: Radar Inertial Odometry with Multiple Radar Sensors and Yaw Aiding


A robust and accurate real-time navigation system is crucial for autonomous robotics. In particular, GNSS denied and poor visual conditions are still very challenging as vision based approaches tend to fail in darkness, direct sunlight, fog, or smoke. Therefore, we are taking advantage of inertial data and FMCW radar sensors as both are not affected by such conditions. In this work, we propose a framework, which uses several 4D mmWave radar sensors simultaneously. The extrinsic calibration of each radar sensor is estimated online. Based on a single radar scan, the 3D ego velocity and optionally yaw measurements based on Manhattan world assumptions are fused. An extensive evaluation with real world datasets is presented. We achieve even better accuracies than state of the art stereo Visual Inertial Odometry (VIO) while being able to cope with degraded visual conditions and requiring only very little computational resources.

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  1. Bloesch, M., Omari, S., Hutter, M., and Siegwart, R., Robust visual inertial odometry using a direct EKF-based approach, in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, pp. 298–304.

  2. Sun, K., Mohta, K., Pfrommer, B., Watterson, M., Liu, S., Mulgaonkar, Y., Taylor, C.J., and Kumar, V., Robust stereo visual inertial odometry for fast autonomous flight, IEEE Robotics and Automation Letters, 2018, vol. 3, no. 2, pp. 965–972.

    Article  Google Scholar 

  3. Qin, T., Li, P., and Shen, S., VINS-Mono: A robust and versatile monocular visual-inertial state estimator, IEEE Transactions on Robotics, 2018, vol. 34, no. 4, pp. 1004–1020.

    Article  Google Scholar 

  4. Khattak, S., Papachristos, C., and Alexis, K., Keyframe-based thermal-inertial odometry, Journal of Field Robotics, 2020, vol. 37, no. 4, pp. 552–579.

    Article  Google Scholar 

  5. Saputra, M.R.U., de Gusmao, P.P.B., Lu, C.X., Almalioglu, Y., Rosa, S., Chen, C., Wahlstroem, J., Wang, W., Markham, A., and Trigoni, N., DeepTIO: A deep thermal-inertial odometry with visual hallucination, IEEE Robotics and Automation Letters, 2020, vol. 5, no. 2.

  6. Zhao, S., Wang, P., Zhang, H., Fang, Z., and Scherer, S., TP-TIO: A robust thermal-inertial odometry with deep thermalpoint, in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  7. Dickmann, J., Klappstein, J., Hahn, M., Appen-rodt, N., Bloecher, H., Werber, K., and Sailer, A., Automotive radar – The key technology for autonomous driving: From detection and ranging to environmental understanding, in 2016 IEEE Radar Conference (RadarConf).

  8. Doer, C. and Trommer, G.F., Radar inertial odometry with online calibration, in 2020 European Navigation Conference (ENC), 2020, pp. 1–10.

  9. Doer, C. and Trommer, G.F., Yaw aided radar inertial odometry using Manhattan world assumptions, in 28th St. Petersburg International Conference on Integrated Navigation Systems (ICINS), 2021.

  10. Kramer, A., Stahoviak, C., Santamaria-Navarro, A., Aghamohammadi, A.-A., and Heckman, C., Radar-inertial ego-velocity estimation for visually degraded environments, in IEEE International Conference on Robotics and Automation, 2020.

  11. Doer, C. and Trommer, G.F., An EKF-based approach to radar inertial odometry, in IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2020, pp. 152–159.

  12. Lu, C.X., Saputra, M.R.U., Zhao, P., Almalioglu, Y., de Gusmao, P.P.B., Chen, C., Sun, K., Trigoni, N., and Markham, A., Milliego: Single-chip mmWave radar aided egomotion estimation via deep sensor fusion, in Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Ser. SenSys ’20, 2020.

  13. Quist, E.B. and Beard, R.W., Radar odometry on fixed-wing small unmanned aircraft, IEEE Transactions on Aerospace and Electronic Systems, 2016, vol. 52, no. 1, pp. 396–410.

    Article  Google Scholar 

  14. Cen, S.H. and Newman, P., Precise ego-motion estimation with millimeter-wave radar under diverse and challenging conditions, in IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2018, pp. 1–8.

  15. Aldera, R., De Martini, D., Gadd, M., and Newman, P., Fast radar motion estimation with a learnt focus of attention using weak supervision, in 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 1190–1196.

  16. Park, Y. S., Shin, Y. S., and Kim, A., Pharao: Direct radar odometry using phase correlation, in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 2617–2623.

  17. Almalioglu, Y., Turan, M., Lu, C.X., Trigoni, N., and Markham, A., Milli-RIO: Ego-motion estimation with low-cost millimetre-wave radar, IEEE Sensors Journal, 2021, vol. 21, no. 3, pp. 3314–3323.

    Article  Google Scholar 

  18. Rapp, M., Barjenbruch, M., Dietmayer, K., Hahn, M., and Dickmann, J., A fast probabilistic ego-motion estimation framework for radar, in 2015 European Conference on Mobile Robots (ECMR), 2015, pp. 1–6.

  19. Kellner, D., Barjenbruch, M., Klappstein, J., Dickmann, J., and Dietmayer, K., Instantaneous ego-motion estimation using Doppler radar, in Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013, pp. 869–874.

  20. Doer, C. and Trommer, G.F., Radar visual inertial odometry and radar thermal inertial odometry: Robust navigation even in challenging visual conditions, in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.

  21. Sola, J., Quaternion kinematics for the error-state KF, 2017.

  22. Roumeliotis, S.I. and Burdick, J.W., Stochastic cloning: A generalized framework for processing relative state measurements, in Proc. 2002 IEEE International Conference on Robotics and Automation, 2002.

  23. Fischler, M.A. and Bolles, R.C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 1981.

  24. Ghilani, C.D., Adjustment Computations: Spatial Data Analysis, Hoboken, New Jersey: John Wiley & Sons, 2017.

    Book  Google Scholar 

  25. Delmerico, J. and Scaramuzza, D., A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots, in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 2502–2509.

  26. Umeyama, S., Least-squares estimation of transformation parameters between two point patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, vol. 13, no. 4, pp. 376–380.

    Article  Google Scholar 

  27. Zhang, Z. and Scaramuzza, D., A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.

  28. Guennebaud, G., Jacob, B., et al., Eigen v3,, 2010.

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Correspondence to C. Doer.

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Doer, C., Trommer, G.F. x-RIO: Radar Inertial Odometry with Multiple Radar Sensors and Yaw Aiding. Gyroscopy Navig. 12, 329–339 (2021).

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  • radar inertial odometry
  • navigation system
  • autonomous robotics