Robust Inference for Visual-Inertial Sensor Fusion
This work addresses robust motion estimation for robotics or autonomous systems, but it appears incremental as it builds on existing methods with approximations.
The paper tackles the problem of robustly inferring 3D motion from visual-inertial sensor fusion by addressing outliers in visual data, proposing optimal and approximate discriminants that improve state-of-the-art performance while maintaining computational efficiency.
Inference of three-dimensional motion from the fusion of inertial and visual sensory data has to contend with the preponderance of outliers in the latter. Robust filtering deals with the joint inference and classification task of selecting which data fits the model, and estimating its state. We derive the optimal discriminant and propose several approximations, some used in the literature, others new. We compare them analytically, by pointing to the assumptions underlying their approximations, and empirically. We show that the best performing method improves the performance of state-of-the-art visual-inertial sensor fusion systems, while retaining the same computational complexity.