Manifold-Optimized Error-State Kalman Filter for Robust Pose Estimation in Unmanned Aerial Vehicles  

作　　者：Bolin Jia Zongwen Bai Yiqun Gao Dong Wang Meili Zhou Peiqi Gao Pei Zhang Zhang Yang 

机构地区：[1]School of Physics and Electronic Information,Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data,Yan’an University,Yan’an 716000,Shaanxi,China [2]Zichang Vegetable Center,Yan’an 716000,Shaanxi,China

出　　处：《Journal of Electronic Research and Application》2025年第2期247-257,共11页电子研究与应用

基　　金：National Natural Science Foundation of China(Grant No.62266045);National Science and Technology Major Project of China(No.2022YFE0138600)。

摘　　要：This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.

关 键 词：UAV pose estimation Error-State Kalman Filter MANIFOLD GPS LIDAR 

分 类 号：V249.1[航空宇航科学与技术—飞行器设计]