State Estimation Task

State estimation focuses on accurately determining the current state of a system, crucial for applications like autonomous driving and robotics. Research currently emphasizes improving the accuracy and robustness of state estimation across diverse modalities (e.g., visual, inertial, multimodal) using various architectures, including transformers, state-space models, and differentiable filters, often tailored to specific challenges like nonlinear dynamics or outlier data. These advancements are driving improvements in real-world applications by enabling more reliable and efficient control, navigation, and interaction with dynamic environments. The development of new metrics and software packages further facilitates research and practical deployment.