Comparison of Delay Compensation Strategies in ESKF Low-Latency Navigation for More-Autonomous Aircraft - Freiberger, Andrei; Dias, Stiven Schwanz; Máximo, Marcos R. O. A. (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS); 2025: Anais do XVII Simpósio Brasileiro de Robótica e XVI Workshop de Robótica na Educação, 2025)
Accurate state estimation is critical for the safe and reliable operation of autonomous aerial vehicles. However, the inherent delay in sensor measurements, such as those from vision-based systems, poses a significant challenge. This study presents a comparative analysis of two delay compensation techniques, Filter Recalculation (FR) and Measurement Extrapolation (ME), for an Error-State Kalman Filter (ESKF) designed for inertial navigation, particularly in GNSS-denied environments. We developed a simulation environment in C++ to model a navigation system fusing IMU and camera data, subjecting it to different measurement delays. The results indicate that the FR method achieves superior accuracy and robustness, nearly eliminating delay-induced errors, but at a significant computational cost. The ME method offers a more computationally efficient alternative but exhibits numerical instability with larger delays. This analysis provides crucial insights for selecting an appropriate delay compensation strategy based on system-specific accuracy and latency requirements.