milliEgo: mmWave Aided Egomotion Estimation with Deep Sensor Fusion

Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness to emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by vision based techniques e.g., visual-inertial odometry, these suffer from challenges with scene illumination or featureless surfaces. As an alternative, we propose \sysname, 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 underdetermined pose transformation problem. Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g. inertial or visual sensor we introduce a mixed attention approach to deep fusion. Through extensive experiments, we demonstrate how mmWave radar outperforms existing state-of-the-art odometry techniques. We also show that the neural architecture can be made highly efficient and suitable for real-time embedded applications.