UWB/IMU 센서 융합 기반 6자유도 위치 및 자세 추정을 위한 강결합 및 약결합 칼만 필터링 기법의 실험적 비교

초록

This paper presents a novel approach for robot pose estimation through a tightly coupled sensor fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data. Our method employs both the Extended Kalman Filter (EKF) and the Error-State Kalman Filter (ESKF) to accurately estimate a robot’s pose, encompassing both position and orientation in three-dimensional space. The tightly coupled strategy enhances robustness, particularly in challenging environments where UWB data quality is compromised, such as under Non-Line-of-Sight (NLOS) conditions, by effectively fusing raw UWB data with IMU measurements. We validate the proposed approach through extensive simulations and real-world experiments, demonstrating significant improvements in accuracy and robustness compared to the loosely coupled method. Our results highlight that the tightly coupled approach, with its superior data fusion capability, particularly excels in altitude (z-axis) estimation, making it highly beneficial for applications requiring precise pose estimation.

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