The field of quadrotor research is moving towards developing more robust and efficient control systems, with a focus on adaptive estimation and navigation in complex environments. Recent studies have explored the use of innovative approaches such as adaptive MARG-only heading estimation, rotor-failure-aware navigation, and data-driven estimation of motor efficiency. These advancements have the potential to significantly improve the accuracy and reliability of quadrotor systems, enabling their use in a wider range of applications. Notable papers in this area include: AMO-HEAD, which presents a lightweight and computationally efficient Extended Kalman Filter framework for heading estimation, and Rotor-Failure-Aware Quadrotors Flight, which demonstrates a rotor-failure-aware navigation system for autonomous flight in unknown environments. Other notable papers include DQ-NMPC, which proposes a novel NMPC framework based on dual-quaternions for quadrotor flight, and Learning Robust Agile Flight Control, which presents a neural-augmented feedback controller for agile flight control with stability guarantees.