Radar-Inertial Ego-Velocity Estimation for Visually Degraded Environments
A. Kramer, C. Stahoviak, A. Santamaria-Navarro, A. Agha-mohammadi and C. Heckman
IEEE International Conference on Robotics and Automation, pp. 5739-5746, Paris, France, 2020.

We present an approach for estimating the body-frame velocity of a mobile robot. We combine measurements from a millimeter-wave radar-on-a-chip sensor and an inertial measurement unit (IMU) in a batch optimization over a sliding window of recent measurements. The sensor suite employed is lightweight, low-power, and is invariant to ambient lighting conditions. This makes the proposed approach an attractive solution for platforms with limitations around payload and longevity, such as aerial vehicles conducting autonomous exploration in perceptually degraded operating conditions, including subterranean environments. We compare our radar-inertial velocity estimates to those from a visual-inertial (VI) approach. We show the accuracy of our method is comparable to VI in conditions favorable to VI, and far exceeds the accuracy of VI when conditions deteriorate.