The field of autonomous systems is witnessing significant advancements in state estimation and control, with a focus on improving accuracy, robustness, and efficiency. Recent developments have led to the creation of novel frameworks, algorithms, and techniques that enhance the performance of autonomous vehicles, aerial systems, and other robotic platforms. Notably, the integration of sensor fusion, machine learning, and model predictive control has enabled more precise and reliable state estimation, while advancements in control strategies have improved the stability and maneuverability of autonomous systems.

Particularly noteworthy papers include: The paper on Robust Online Calibration for UWB-Aided Visual-Inertial Navigation with Bias Correction, which presents a novel calibration framework that addresses the challenges of accurate anchor positioning and robust initialization. The paper on Towards Fully Onboard State Estimation and Trajectory Tracking for UAVs with Suspended Payloads, which proposes a framework that uses standard onboard sensors to estimate and control the payload's position, demonstrating strong robustness to variations in payload parameters. The paper on AutoMPC, which aims to address the challenges of integrating Model Predictive Control into industrial production vehicles by providing a code generator for MPC-based automated driving, enabling easy implementation and high customizability. The paper on Lightweight Tracking Control for Computationally Constrained Aerial Systems with the Newton-Raphson Method, which investigates the performance of a lightweight tracking controller applied to aerial hardware platforms, showing comparable or superior tracking performance to established control frameworks with reduced computation time and energy expenditure.