Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry
This work addresses sensor degradation in VIO for robotics or autonomous systems, offering an incremental improvement with a novel method for a known bottleneck.
The paper tackles monocular Visual-Inertial Odometry by proposing an end-to-end multi-modal learning approach that uses multi-head self-attention to learn multiplicative interactions and incorporates model uncertainty via scalable Laplace Approximation, achieving superior performance compared to state-of-the-art methods on the KITTI dataset.
This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in the light of sensor degradation scenarios. The proposed network makes use of a multi-head self-attention mechanism that learns multiplicative interactions between multiple streams of information. Another design feature of our approach is the incorporation of the model uncertainty using scalable Laplace Approximation. We evaluate the performance of the proposed approach by comparing it against the end-to-end state-of-the-art methods on the KITTI dataset and show that it achieves superior performance. Importantly, our work thereby provides an empirical evidence that learning multiplicative interactions can result in a powerful inductive bias for increased robustness to sensor failures.