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Masking By Moving (MByM), provides robust and accurate radar odometrymeasurements through an exhaustive correlative search across discretised posecandidates. However, this dense search creates a significant computationalbottleneck which hinders real-time performance when high-end GPUs are notavailable. Utilising the translational invariance of the Fourier Transform, inour approach, f-MByM, we decouple the search for angle and translation. Bymaintaining end-to-end differentiability a neural network is used to mask scansand trained by supervising pose prediction directly. Training faster and withless memory, utilising a decoupled search allows f-MByM to achieve significantrun-time performance improvements on a CPU (168%) and to run in real-time onembedded devices, in stark contrast to MByM. Throughout, our approach remainsaccurate and competitive with the best radar odometry variants available in theliterature -- achieving an end-point drift of 2.01% in translation and6.3deg/km on the Oxford Radar RobotCar Dataset.

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