Emerging Trends in Pedestrian Inertial Positioning: Neural Network-Based Methods, Performance, and Future Prospects
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摘要: 行人惯性定位通过惯性测量单元 (Inertial measurement unit, IMU) 的测量序列来估计行人的位置, 近年来已成为解决室内或卫星信号遮挡环境下的行人自主定位的重要手段. 然而, 传统惯性定位算法在双重积分时易受误差源影响导致漂移问题, 一定程度上限制了行人惯性定位在长时间长距离实际运动中的应用. 幸运的是, 基于神经网络学习的方式能够仅从IMU历史数据中学习行人的运动模式并修正惯性测量值在积分时引起的漂移. 为此, 本文对近期基于深度神经网络的行人惯性定位进行全面综述. 首先对传统的惯性定位算法进行了简要介绍; 其次, 按照是否融入领域知识分别介绍了端到端的神经惯性定位方法和融合领域知识的神经惯性定位算法的研究动态; 然后, 概述了行人惯性定位的基准数据集、评价指标, 并分析比较了其中一些代表性方法的优势和不足; 最后, 对该领域需要解决的关键难点问题进行了总结, 并探讨基于深度神经网络的行人惯性定位未来所面临的关键挑战与发展趋势, 以期为后续的研究提供有益参考.Abstract: Pedestrian inertial positioning, which estimates a pedestrian's location using measurement sequences from an inertial measurement unit (IMU), has become an important solution for pedestrian autonomous positioning in indoor environments or areas with satellite signal blockages in recent years. However, traditional inertial positioning algorithms are prone to drift issues during double integration due to the influence of error sources, which to some extent limits the application of pedestrian inertial positioning in long-term, long-distance real-world movements. Fortunately, neural network-based approaches can learn pedestrian motion patterns from historical IMU data and correct the drift caused by inertial measurement during integration. Therefore, this paper presents a comprehensive review of recent developments in pedestrian inertial positioning based on deep neural networks. First, a brief introduction to traditional inertial positioning algorithms is provided. Next, the latest research on end-to-end neural inertial positioning methods and neural inertial positioning algorithms incorporating domain knowledge is reviewed. Following that, pedestrian inertial positioning benchmark datasets and evaluation metrics are summarized, and the advantages and disadvantages of some representative methods are analyzed and compared. Finally, the key challenges that need to be addressed in this field are summarized, and the critical challenges and future trends of pedestrian inertial positioning based on deep neural networks are discussed, with the aim of providing useful references for future research.1)
1 1https://www.dropbox.com/s/9zzaj3h3u4bta23/ridi_data_publish_v2.zip?dl=0 2https://github.com/higerra/TangoIMURecorder 2)2 3http://deepio.cs.ox.ac.uk/ 4https://ronin.cs.sfu.ca/ -
图 1 行人惯性定位范式. 行人惯性定位大多基于智能手机、智能手环和AR/VR等移动设备中的惯性传感器来实现
Fig. 1 Pedestrian Inertial Localization Paradigm. Pedestrian inertial localization is mostly implemented using inertial sensors in mobile devices such as smartphones, smartwatches, and AR/VR devices
图 6 基于神经网络的行人惯性定位范式
Fig. 6 Paradigm of pedestrian inertial positioning based on neural network
表 1 基于神经网络的行人惯性定位方法概览
Table 1 Overview of neural network-based pedestrian inertial positioning methods
方法 时间 模型 学习方式 方法特征 IONet[43] 2018 LSTM 监督 将惯性定位问题转化为序列学习问题, 并基于LSTM来学习位移并构造惯性里程计 L-IONet[46] 2020 WaveNet 监督 利用自回归模型替换LSTM来处理惯性长连续信号并预测极坐标系下的位移 Motiontransformer[49] 2019 LSTM 监督 通过生成对抗网络和域适应来学习一个领域不变的语义表示 TLIO[52] 2020 ConvNet 监督 基于ConvNet回归相对位移和不确定性并将二者合并到卡尔曼滤波器进行状态估计 RoNIN[27] 2020 ConvNet/LSTM 监督 基于ConvNet/LSTM从惯性数据中预测行人的2D速度向量 Wang等[54] 2021 ConvNet 监督 通过ResNet网络来回归速度大小和移动角度 IMUNet[55] 2024 ConvNet 监督 使用深度和点卷积替换传统卷积操作提高模型推理速度 RIO[56] 2022 ConvNet 自监督 引入了旋转等方差作为强大的自监督信号来训练惯性定位模型 HNNTA[58] 2022 ConvNet/LSTM 监督 利用时间注意机制对LSTM产生的隐藏状态进行加权 RBCN[59] 2023 ConvNet/LSTM 监督 利用多种混合注意力机制增强网络对通道和空间特征的学习能力 Res2Net[60] 2022 ConvNet 监督 融入了Res2Net模块来提取更加细粒度的特征表示 CTIN[26] 2022 Transformer 监督 首个基于Transformer来融合空间表示与时间知识的模型 RIOT[63] 2023 Transformer 监督 通过结合真实位置先验递归的学习运动特征和系统误差偏差 NILoc[64] 2022 Transformer 监督 将独一无二的人体运动模式映射成行人位置 IDOL[65] 2021 LSTM 监督 将行人惯性定位分为方向估计和位置估计两个阶段 Shao等[67] 2018 ConvNet 监督 基于深度卷积神经网络的步长检测方案, 以提高计步器的鲁棒性 Ren等[68] 2021 LSTM 监督 设计了一种基于LSTM的步态计数器 WAIT[69] 2023 ConvNet 监督 利用自动编码器将IMU测量值转化为无误差的波形并提取各种与移动性相关的信息 Gu等[70] 2018 — 监督 基于堆叠的自动编码器的步长估计模型 StepNet[72] 2020 ConvNet 监督 基于CNN动态的回归回归步长或距离的变化 Wang等[73] 2019 LSTM 监督 在步长估计模型中加入变分自编码自动消除特征向量中的固有噪声 Manos等[79] 2022 ConvNet 监督 利用时间卷积和多尺度注意层提取运动矢量进行航向估计 PDRNet[80] 2022 ConvNet 监督 基于ResNet设计了一个位置识别和一个获取距离和航向变化的回归网络 Wagstaff[81] 2018 LSTM 监督 用LSTM代替标准零速度检测器来辅助惯性导航系统的方法 Yu等[82] 2019 ConvNet 监督 一种基于卷积神经网络的零速度点探测器 Bo等[74] 2022 ResNet/GRU 无监督 利用对抗训练和子类分类器来构建一个多源无监督域适应网络 *注释: 上述方法根据是否融入领域知识分为两类. 
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表 2 行人惯性定位数据集
Table 2 Pedestrian inertial localization datasets
数据集 时间 采样频率 IMU载体 真值 数据集大小(轨迹数) 设备携带方式 RIDI[83] 2017 200 Hz Lenovo Phab2 Pro Tango手机 74 裤兜、包、手持、身体 TUM VI[84] 2018 200 Hz — 动作捕捉系统 28 手持 OXIOD[85] 2018 100 Hz iPhone 5/6/7 Plus,Nexus 5 动作捕捉系统 158 手持、口袋、手袋、推车 RoNIN[27] 2019 200 Hz Galaxy S9, Pixel 2 XL AR设备 276 自然携带 IDOL[65] 2020 100 Hz iPhone 8 Kaarta Stencil 84 自然携带 CTIN[26] 2021 200 Hz Samsung Note, Galaxy Google ARCore 100 自然携带 SIMD[86] 2023 50 Hz 多种型号智能手机 GPS/IMU 4562 自然携带
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表 3 在RIDI测试数据集上的行人惯性定位方法对比 (单位: m)
Table 3 Comparison of pedestrian inertial positioning methods on the RIDI test dataset (unit: meter)
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表 4 在OXIOD测试数据集上的行人惯性定位方法对比 (单位: m)
Table 4 Comparison of pedestrian inertial positioning methods on the OXIOD test dataset (unit: meter)
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表 5 在RoNIN测试数据集上的行人惯性定位方法对比 (单位: m)
Table 5 Comparison of pedestrian inertial positioning methods on the RoNIN test dataset (unit: meter)
模型 seen-AE seen-RE unseen-AE unseen-RE SINS[25] 675.21 169.48 458.06 117.06 PDR[29] 29.54 21.36 27.67 23.17 RIDI[83] 17.06 17.50 15.66 18.91 R-LSTM[27] 4.18 2.63 5.32 3.58 R-ResNet[27] 3.54 2.67 5.14 4.37 R-TCN[27] 4.38 2.90 5.70 4.07 *注释: 上述所有实验度量结果来源于文献[27]. 其中, “seen”表示测试集和训练集的被试相同; “unseen” 表示测试集中的被试在训练集中未出现过; “AE” 表示绝对轨迹误差; “RE” 表示相对轨迹误差.
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