  Robust and accurate trajectory estimation of mobile agents such as people and
robots is a key requirement for providing spatial awareness for emerging
capabilities such as augmented reality or autonomous interaction. Although
currently dominated by optical techniques e.g., visual-inertial odometry, these
suffer from challenges with scene illumination or featureless surfaces. As an
alternative, we propose milliEgo, a novel deep-learning approach to robust
egomotion estimation which exploits the capabilities of low-cost mmWave radar.
Although mmWave radar has a fundamental advantage over monocular cameras of
being metric i.e., providing absolute scale or depth, current single chip
solutions have limited and sparse imaging resolution, making existing
point-cloud registration techniques brittle. We propose a new architecture that
is optimized for solving this challenging pose transformation problem.
Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g.
inertial or visual sensors we introduce a mixed attention approach to deep
fusion. Through extensive experiments, we demonstrate our proposed system is
able to achieve 1.3% 3D error drift and generalizes well to unseen
environments. We also show that the neural architecture can be made highly
efficient and suitable for real-time embedded applications.
