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摘要翻译：
MEMS IMU与磁强计的传感器融合是一种流行的系统设计，因为这种9自由度系统能够实现无漂移的三维方向跟踪。然而，这些系统往往容易受到环境磁畸变的影响，并且缺乏有用的位置信息；在没有外部位置辅助（如卫星/超宽带定位系统）的情况下，9自由度MEMS IMU的定位精度由于未建模误差而迅速恶化。定位信息在许多没有卫星的地理信息学应用（例如室内导航、基于位置的服务等）中是有价值的。提出了一种改进的基于批量优化的9自由度IMU室内位姿跟踪方法。采用一种鲁棒的现场用户自标定方法，在紧耦合后处理最小二乘框架中同时建模加速度计、陀螺仪和磁强计的系统误差，可以提高9自由度MEMS IMU估计轨迹的精度。通过相对磁测量更新和稳健权函数的组合，该方法能够容忍高水平的磁畸变。提出的自动校准方法在各种不同的磁场条件下进行了测试，以模拟一个人带着传感器在口袋里走路，一个人检查手机，一个人带着智能手表走路。在实验中，该算法使定位精度提高了79.8～89.5%,定位精度提高了72.9～92.8%,使相对定位误差在10秒积分后从米级降低到分米级，而不需要对用户的动态特性作任何假设。
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英文标题：
《Statistical Sensor Fusion of a 9-DoF MEMS IMU for Indoor Navigation》
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作者：
Jacky C.K. Chow
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8 - 89.5% and the dead-reckoned positioning accuracy by 72.9 - 92.8%, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.
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PDF链接：
https://arxiv.org/pdf/1802.04388
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