Equivariant Filter

Equivariant filters are a class of algorithms designed to leverage the inherent symmetries within data to improve the efficiency and robustness of estimation and learning tasks. Current research focuses on applying equivariant filters to diverse problems, including simultaneous localization and mapping (SLAM), inertial navigation, and molecular property prediction, often employing Lie group symmetries and diffusion models within the filter design. This approach offers significant advantages over traditional methods by reducing linearization errors, improving sample efficiency, and enhancing generalization capabilities, leading to more accurate and reliable results in various applications.