Fast-MbyM: Leveraging The Translational Invariance of the Fourier Transform for Fast and Accurate Radar Odometry #

Here you will find some supporting material for my most recent submission to ICRA 2022. More will be added over the next few months. Watch this space...


Slides #

Abstract #

The current state of the art in radar odometry, MbyM, provides robust and accurate odometry measurements through an exhaustive correlative search across discretised pose candidates. However, this dense search creates a significant computational bottleneck which hinders real-time performance when high-end GPUs are not available. Utilising the translational invariance of the Fourier Transform, in our approach, f-MbyM, we decouple the search for angle and translation. By maintaining end-to-end differentiability a neural network is used to mask scans and trained by supervising pose prediction directly. Training faster and with less memory, utilising a decoupled search allows f-MbyM to achieve significant run-time performance improvements on a CPU and to run in real-time on embedded devices, in stark contrast to MbyM. Throughout, our approach remains accurate and competitive with the best radar odometry variants available in the literature – achieving an end-point drift of 2.01% in translation and 6.3deg/km on the Oxford Radar RobotCar Dataset.

Video #

Contact #

If you have any other questions, drop me an email.