Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information
This work addresses robust localization for autonomous vehicles in urban environments, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the problem of robust, real-time pose estimation in radar odometry by learning a distraction-free embedding space, resulting in a 68% error reduction and an order of magnitude speed improvement over previous state-of-the-art methods.
This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor objects. The system deploys a fully differentiable, correlation-based radar matching approach. This provides the same level of interpretability as established scan-matching methods and allows for a principled derivation of uncertainty estimates. The system is trained in a (self-)supervised way using only previously obtained pose information as a training signal. Using 280km of urban driving data, we demonstrate that our approach outperforms the previous state-of-the-art in radar odometry by reducing errors by up 68% whilst running an order of magnitude faster.