milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion
This addresses trajectory estimation for applications like augmented reality and autonomous interaction, offering an alternative to optical methods that struggle with illumination or featureless surfaces, though it is incremental as it builds on existing sensor fusion techniques.
The paper tackles robust egomotion estimation for mobile agents by proposing milliEgo, a deep-learning approach that fuses low-cost mmWave radar with other sensors, achieving 1.3% 3D error drift and generalization to unseen environments.
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.