English

Need for Speed: Fast Correspondence-Free Lidar-Inertial Odometry Using Doppler Velocity

Robotics 2023-10-03 v2

Abstract

In this paper, we present a fast, lightweight odometry method that uses the Doppler velocity measurements from a Frequency-Modulated Continuous-Wave (FMCW) lidar without data association. FMCW lidar is a recently emerging technology that enables per-return relative radial velocity measurements via the Doppler effect. Since the Doppler measurement model is linear with respect to the 6-degrees-of-freedom (DOF) vehicle velocity, we can formulate a linear continuous-time estimation problem for the velocity and numerically integrate for the 6-DOF pose estimate afterward. The caveat is that angular velocity is not observable with a single FMCW lidar. We address this limitation by also incorporating the angular velocity measurements from a gyroscope. This results in an extremely efficient odometry method that processes lidar frames at an average wall-clock time of 5.64ms on a single thread, well below the 10Hz operating rate of the lidar we tested. We show experimental results on real-world driving sequences and compare against state-of-the-art Iterative Closest Point (ICP)-based odometry methods, presenting a compelling trade-off between accuracy and computation. We also present an algebraic observability study, where we demonstrate in theory that the Doppler measurements from multiple FMCW lidars are capable of observing all 6 degrees of freedom (translational and angular velocity).

Keywords

Cite

@article{arxiv.2303.06511,
  title  = {Need for Speed: Fast Correspondence-Free Lidar-Inertial Odometry Using Doppler Velocity},
  author = {David J. Yoon and Keenan Burnett and Johann Laconte and Yi Chen and Heethesh Vhavle and Soeren Kammel and James Reuther and Timothy D. Barfoot},
  journal= {arXiv preprint arXiv:2303.06511},
  year   = {2023}
}

Comments

Accepted and presented at IROS 2023