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

arXiv: https://arxiv.org/abs/2203.00459 · HTML: https://mlsanity.com/paper/2203.00459/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [Open-RadVLAD: Fast and Robust Radar Place Recognition](https://mlsanity.com/paper/2401.15380/) (2024)
- [DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration](https://mlsanity.com/paper/2206.05707/) (2022)

## Abstract (arXiv)

Masking By Moving (MByM), provides robust and accurate radar 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 (168%) 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.