A reliable self-contained navigation system is essential for autonomous vehicles. Based on our previous study on Wheel-INS \cite{niu2019}, a wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning (DR) system, in this paper, we propose a multiple IMUs-based DR solution for the wheeled robots. The IMUs are mounted at different places of the wheeled vehicles to acquire various dynamic information. In particular, at least one IMU has to be mounted at the wheel to measure the wheel velocity and take advantages of the rotation modulation. The system is implemented through a distributed extended Kalman filter structure where each subsystem (corresponding to each IMU) retains and updates its own states separately. The relative position constraints between the multiple IMUs are exploited to further limit the error drift and improve the system robustness. Particularly, we present the DR systems using dual Wheel-IMUs, one Wheel-IMU plus one vehicle body-mounted IMU (Body-IMU), and dual Wheel-IMUs plus one Body-IMU as examples for analysis and comparison. Field tests illustrate that the proposed multi-IMU DR system outperforms the single Wheel-INS in terms of both positioning and heading accuracy. By comparing with the centralized filter, the proposed distributed filter shows unimportant accuracy degradation while holds significant computation efficiency. Moreover, among the three multi-IMU configurations, the one Body-IMU plus one Wheel-IMU design obtains the minimum drift rate. The position drift rates of the three configurations are 0.82\% (dual Wheel-IMUs), 0.69\% (one Body-IMU plus one Wheel-IMU), and 0.73\% (dual Wheel-IMUs plus one Body-IMU), respectively.

A reliable pose estimator robust to environmental disturbances is desirable for mobile robots. To this end, inertial measurement units (IMUs) play an important role because they can perceive the full motion state of the vehicle independently. However, it suffers from accumulative error due to inherent noise and bias instability, especially for low-cost sensors. In our previous studies on Wheel-INS \cite{niu2021, wu2021}, we proposed to limit the error drift of the pure inertial navigation system (INS) by mounting an IMU to the wheel of the robot to take advantage of rotation modulation. However, it still drifted over a long period of time due to the lack of external correction signals. In this letter, we propose to exploit the environmental perception ability of Wheel-INS to achieve simultaneous localization and mapping (SLAM) with only one IMU. To be specific, we use the road bank angles (mirrored by the robot roll angles estimated by Wheel-INS) as terrain features to enable the loop closure with a Rao-Blackwellized particle filter. The road bank angle is sampled and stored according to the robot position in the grid maps maintained by the particles. The weights of the particles are updated according to the difference between the currently estimated roll sequence and the terrain map. Field experiments suggest the feasibility of the idea to perform SLAM in Wheel-INS using the robot roll angle estimates. In addition, the positioning accuracy is improved significantly (more than 30\%) over Wheel-INS. Source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-SLAM).

滑动检测对于在外星人表面驾驶的流浪者的安全性和效率至关重要。当前的行星流动站滑移检测系统依赖于视觉感知，假设可以在环境中获得足够的视觉特征。然而，基于视觉的方法容易受到感知降解的行星环境，具有主要低地形特征，例如岩石岩，冰川地形，盐散发物以及较差的照明条件，例如黑暗的洞穴和永久阴影区域。仅依靠视觉传感器进行滑动检测也需要额外的计算功率，并降低了流动站的遍历速率。本文回答了如何检测行星漫游者的车轮滑移而不取决于视觉感知的问题。在这方面，我们提出了一个滑动检测系统，该系统从本体感受的本地化框架中获取信息，该框架能够提供数百米的可靠，连续和计算有效的状态估计。这是通过使用零速度更新，零角度更新和非独立限制作为惯性导航系统框架的伪测量更新来完成的。对所提出的方法进行了对实际硬件的评估，并在行星 - 分析环境中进行了现场测试。该方法仅使用IMU和车轮编码器就可以达到150 m左右的92％滑动检测精度。

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

安装在微空中车辆（MAV）上的地面穿透雷达是有助于协助人道主义陆地间隙的工具。然而，合成孔径雷达图像的质量取决于雷达天线的准确和精确运动估计以及与MAV产生信息性的观点。本文介绍了一个完整的自动空气缩进的合成孔径雷达（GPSAR）系统。该系统由空间校准和时间上同步的工业级传感器套件组成，使得在地面上方，雷达成像和光学成像。自定义任务规划框架允许在地上控制地上的Stripmap和圆形（GPSAR）轨迹的生成和自动执行，以及空中成像调查飞行。基于因子图基于Dual接收机实时运动（RTK）全局导航卫星系统（GNSS）和惯性测量单元（IMU）的测量值，以获得精确，高速平台位置和方向。地面真理实验表明，传感器时机为0.8美元，正如0.1美元的那样，定位率为1 kHz。与具有不确定标题初始化的单个位置因子相比，双位置因子配方可提高高达40％，批量定位精度高达59％。我们的现场试验验证了本地化准确性和精度，使得能够相干雷达测量和检测在沙子中埋入的雷达目标。这验证了作为鸟瞰着地图检测系统的潜力。

A monocular visual-inertial system (VINS), consisting of a camera and a low-cost inertial measurement unit (IMU), forms the minimum sensor suite for metric six degreesof-freedom (DOF) state estimation. However, the lack of direct distance measurement poses significant challenges in terms of IMU processing, estimator initialization, extrinsic calibration, and nonlinear optimization. In this work, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization and failure recovery. A tightly-coupled, nonlinear optimization-based method is used to obtain high accuracy visual-inertial odometry by fusing pre-integrated IMU measurements and feature observations. A loop detection module, in combination with our tightly-coupled formulation, enables relocalization with minimum computation overhead. We additionally perform four degrees-of-freedom pose graph optimization to enforce global consistency. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform onboard closed-loop autonomous flight on the MAV platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy localization. We open source our implementations for both PCs 1 and iOS mobile devices 2 .

惯性导航系统与全球导航卫星系统之间的融合经常用于许多平台，例如无人机，陆地车辆和船舶船只。融合通常是在基于模型的扩展卡尔曼过滤框架中进行的。过滤器的关键参数之一是过程噪声协方差。它负责实时解决方案的准确性，因为它考虑了车辆动力学不确定性和惯性传感器质量。在大多数情况下，过程噪声被认为是恒定的。然而，由于整个轨迹的车辆动力学和传感器测量变化，过程噪声协方差可能会发生变化。为了应对这种情况，文献中建议了几种基于自适应的Kalman过滤器。在本文中，我们提出了一个混合模型和基于学习的自适应导航过滤器。我们依靠基于模型的Kalman滤波器和设计深神网络模型来调整瞬时系统噪声协方差矩阵，仅基于惯性传感器读数。一旦学习了过程噪声协方差，就可以将其插入建立的基于模型的Kalman滤波器中。在推导了提出的混合框架后，提出了使用四极管的现场实验结果，并给出了与基于模型的自适应方法进行比较。我们表明，所提出的方法在位置误差中获得了25％的改善。此外，提出的混合学习方法可以在任何导航过滤器以及任何相关估计问题中使用。

自动水下车辆（AUV）通常在许多水下应用中使用。最近，在文献中，多旋翼无人自动驾驶汽车（UAV）的使用引起了更多关注。通常，两个平台都采用惯性导航系统（INS）和协助传感器进行准确的导航解决方案。在AUV导航中，多普勒速度日志（DVL）主要用于帮助INS，而对于无人机，通常使用全球导航卫星系统（GNSS）接收器。辅助传感器和INS之间的融合需要在估计过程中定义步长参数。它负责解决方案频率更新，并最终导致其准确性。步长的选择在计算负载和导航性能之间构成了权衡。通常，与INS操作频率（数百个HERTZ）相比，帮助传感器更新频率要慢得多。对于大多数平台来说，这种高率是不必要的，特别是对于低动力学AUV。在这项工作中，提出了基于监督机器学习的自适应调整方案，以选择适当的INS步骤尺寸。为此，定义了一个速度误差，允许INS/DVL或INS/GNSS在亚最佳工作条件下起作用，并最大程度地减少计算负载。模拟和现场实验的结果显示了使用建议的方法的好处。此外，建议的框架可以应用于任何类型的传感器或平台之间的任何其他融合场景。

我们为腿部机器人提供了一个开源视觉惯性训练率（VILO）状态估计解决方案Cerberus，该机器人使用一组标准传感器（包括立体声摄像机，IMU，联合编码器，，imu，联合编码器）实时实时估算各个地形的位置和接触传感器。除了估计机器人状态外，我们还执行在线运动学参数校准并接触离群值拒绝以大大减少位置漂移。在各种室内和室外环境中进行的硬件实验验证了Cerberus中的运动学参数可以将估计的漂移降低到长距离高速运动中的1％以下。我们的漂移结果比文献中报道的相同的一组传感器组比任何其他状态估计方法都要好。此外，即使机器人经历了巨大的影响和摄像头遮挡，我们的状态估计器也表现良好。状态估计器的实现以及用于计算我们结果的数据集，可在https://github.com/shuoyangrobotics/cerberus上获得。

GNSS and LiDAR odometry are complementary as they provide absolute and relative positioning, respectively. Their integration in a loosely-coupled manner is straightforward but is challenged in urban canyons due to the GNSS signal reflections. Recent proposed 3D LiDAR-aided (3DLA) GNSS methods employ the point cloud map to identify the non-line-of-sight (NLOS) reception of GNSS signals. This facilitates the GNSS receiver to obtain improved urban positioning but not achieve a sub-meter level. GNSS real-time kinematics (RTK) uses carrier phase measurements to obtain decimeter-level positioning. In urban areas, the GNSS RTK is not only challenged by multipath and NLOS-affected measurement but also suffers from signal blockage by the building. The latter will impose a challenge in solving the ambiguity within the carrier phase measurements. In the other words, the model observability of the ambiguity resolution (AR) is greatly decreased. This paper proposes to generate virtual satellite (VS) measurements using the selected LiDAR landmarks from the accumulated 3D point cloud maps (PCM). These LiDAR-PCM-made VS measurements are tightly-coupled with GNSS pseudorange and carrier phase measurements. Thus, the VS measurements can provide complementary constraints, meaning providing low-elevation-angle measurements in the across-street directions. The implementation is done using factor graph optimization to solve an accurate float solution of the ambiguity before it is fed into LAMBDA. The effectiveness of the proposed method has been validated by the evaluation conducted on our recently open-sourced challenging dataset, UrbanNav. The result shows the fix rate of the proposed 3DLA GNSS RTK is about 30% while the conventional GNSS-RTK only achieves about 14%. In addition, the proposed method achieves sub-meter positioning accuracy in most of the data collected in challenging urban areas.

自主飞机的导航系统依赖于由套件的读数提供的读数来估计飞机状态。在固定翼车的情况下，传感器套件由三联脉的加速度计，陀螺仪和磁力计，全球导航卫星系统（GNSS）接收器和空中数据系统（皮托管，空气叶片，温度计和晴雨表）组成，并且通常由一个或多个数码相机补充。准确表示每个传感器的行为和错误源，以及摄像机生成的图像，在飞行模拟中是必不可少的，以及对新型惯性或视觉导航算法的评估，以及在低交换的情况下大小，重量和电源）飞机，其中传感器的质量和价格有限。本文为每个传感器提供了现实和可定制的模型，该传感器已被实现为开源C ++模拟。随着时间的推移提供了飞机状态的真正变化，模拟提供了所有传感器产生的误差的时间戳系列，以及地球表面的现实图像，类似于沿着指示的状态位置飞行的真正摄像机飞行的地面表面和态度。

我们在本文中介绍Raillomer，实现实时准确和鲁棒的内径测量和轨道车辆的测绘。 Raillomer从两个Lidars，IMU，火车车程和全球导航卫星系统（GNSS）接收器接收测量。作为前端，来自IMU / Royomer缩放组的估计动作De-Skews DeSoised Point云并为框架到框架激光轨道测量产生初始猜测。作为后端，配制了基于滑动窗口的因子图以共同优化多模态信息。另外，我们利用来自提取的轨道轨道和结构外观描述符的平面约束，以进一步改善对重复结构的系统鲁棒性。为了确保全局常见和更少的模糊映射结果，我们开发了一种两级映射方法，首先以本地刻度执行扫描到地图，然后利用GNSS信息来注册模块。该方法在聚集的数据集上广泛评估了多次范围内的数据集，并且表明Raillomer即使在大或退化的环境中也能提供排入量级定位精度。我们还将Raillomer集成到互动列车状态和铁路监控系统原型设计中，已经部署到实验货量交通铁路。

通过实现复杂场景实现长期漂移相机姿势估计的目标，我们提出了一种全球定位框架，融合了多层的视觉，惯性和全球导航卫星系统（GNSS）测量。不同于以前的松散和紧密耦合的方法，所提出的多层融合允许我们彻底校正视觉测量仪的漂移，并在GNSS降解时保持可靠的定位。特别地，通过融合GNSS的速度，在紧紧地集成的情况下，解决视觉测量测量测量测量率和偏差估计中的尺度漂移和偏差估计的问题的问题，惯性测量单元（IMU）的预集成以及紧密相机测量的情况下 - 耦合的方式。在外层中实现全局定位，其中局部运动进一步与GNSS位置和基于长期时期的过程以松散耦合的方式融合。此外，提出了一种专用的初始化方法，以保证所有状态变量和参数的快速准确估计。我们为室内和室外公共数据集提供了拟议框架的详尽测试。平均本地化误差减少了63％，而初始化精度与最先进的工程相比，促销率为69％。我们已将算法应用于增强现实（AR）导航，人群采购高精度地图更新等大型应用。

移动机器人用于工业，休闲和军事应用。在某些情况下，机器人导航解决方案仅依赖于惯性传感器，因此，导航解决方案会及时漂移。在本文中，我们提出了MORPI框架，这是一种移动机器人纯惯性方法。机器人没有以直线轨迹行进，而是以周期性运动轨迹移动，以实现峰值估计。以这种方式，使用经验公式来估计行进距离，而不是进行三个集成来计算经典惯性解决方案中的机器人位置。提出了两种类型的MORPI方法，其中一种方法基于加速度计和陀螺仪读数，而另一种仅基于陀螺仪。封闭形式的分析溶液被得出表明，与经典的纯惯性溶液相比，MORPI产生较低的位置误差。此外，为了评估所提出的方法，使用配备两种类型的惯性传感器的移动机器人进行现场实验。总共收集了143个轨迹，持续时间为75分钟并评估。结果表明使用我们的方法的好处。为了促进拟议方法的进一步开发，数据集和代码均可在https://github.com/ansfl/morpi上公开获得。

本文解决了现场机器人动态运动下动态在线估计和3轴磁力计的硬铁和软铁偏置的动态在线估计和补偿问题，利用了3轴磁力计和3轴角度的偏置测量速率传感器。所提出的磁力计和角速度偏差估计器（MAVBE）利用了对经受角速度偏移的磁力计信号的非线性处理动态的15状态过程模型，同时估计9个磁力计偏置参数和3个角速率传感器偏置参数，在扩展卡尔曼过滤器框架。偏置参数局部可操作性在数值评估。偏置补偿信号与3轴加速度计信号一起用于估计偏置补偿磁力大地测量标题。与Chesapeake Bay，MD，MD，MD，MD，MD，MD，MD，MD，MD的数值模拟，实验室实验和全规模场试验中，评估了所提出的MAVBE方法的性能。对于所提出的Mavbe，（i）仪器态度不需要估计偏差，结果表明（ii）偏差是本地可观察的，（iii）偏差估计迅速收敛到真正的偏置参数，（iv）仅适用于适度的仪器偏压估计收敛需要激发，并且（v）对磁力计硬铁和柔软铁偏差的补偿显着提高了动态前线估计精度。

在这项工作中，我们展示了基于全球导航卫星系统（GNSS）的零速度信息的重要性。在文献中已经示出了使用零速度更新（Zupt）的零速度信息的有效性已经显示在文献中。在这里，我们利用此信息并将其添加为GNSS因子图中的位置约束。我们还将其性能与GNSS /惯用导航系统（INS）耦合因子图进行比较。我们在三个数据集上测试了我们的Zupt辅助因子图方法，并将其与仅限GNSS因子图进行了比较。

Accurate and smooth global navigation satellite system (GNSS) positioning for pedestrians in urban canyons is still a challenge due to the multipath effects and the non-light-of-sight (NLOS) receptions caused by the reflections from surrounding buildings. The recently developed factor graph optimization (FGO) based GNSS positioning method opened a new window for improving urban GNSS positioning by effectively exploiting the measurement redundancy from the historical information to resist the outlier measurements. Unfortunately, the FGO-based GNSS standalone positioning is still challenged in highly urbanized areas. As an extension of the previous FGO-based GNSS positioning method, this paper exploits the potential of the pedestrian dead reckoning (PDR) model in FGO to improve the GNSS standalone positioning performance in urban canyons. Specifically, the relative motion of the pedestrian is estimated based on the raw acceleration measurements from the onboard smartphone inertial measurement unit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Doppler measurements, and relative motion from PDR are integrated using the FGO. Given the context of pedestrian navigation with a small acceleration most of the time, a novel soft motion model is proposed to smooth the states involved in the factor graph model. The effectiveness of the proposed method is verified step-by-step through two datasets collected in dense urban canyons of Hong Kong using smartphone-level GNSS receivers. The comparison between the conventional extended Kalman filter, several existing methods, and FGO-based integration is presented. The results reveal that the existing FGO-based GNSS standalone positioning is highly complementary to the PDR's relative motion estimation. Both improved positioning accuracy and trajectory smoothness are obtained with the help of the proposed method.

与单个IMU相比，多个刚性连接的惯性测量单元（IMU）传感器提供了更丰富的数据流。最先进的方法遵循IMU测量的概率模型，基于在贝叶斯框架下组合的错误的随机性质。但是，负担得起的低级IMU此外，由于其不受相应的概率模型所掩盖的缺陷而遭受了系统的错误。在本文中，我们提出了一种方法，即合并多个IMU（MIMU）传感器数据的最佳轴组成（BAC），以进行准确的3D置置估计，该数据通过从集合中动态选择最佳的IMU轴来考虑随机和系统误差所有可用的轴。我们在MIMU视觉惯性传感器上评估了我们的方法，并将方法的性能与MIMU数据融合的最新方法进行比较。我们表明，BAC的表现优于后者，并且在开放环路中的方向和位置估计都可以提高20％的精度，但需要适当的处理以保持获得的增益。

We propose a multisensor fusion framework for onboard real-time navigation of a quadrotor in an indoor environment, by integrating sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localization system. The sensor readings from the camera-based object detection algorithm and the UWB localization system arrive intermittently, since the measurements are not readily available. We design a Kalman filter that manages intermittent observations in order to handle and fuse the readings and estimate the pose of the quadrotor for tracking a predefined trajectory. The system is implemented via a Hardware-in-the-loop (HIL) simulation technique, in which the dynamic model of the quadrotor is simulated in an open-source 3D robotics simulator tool, and the whole navigation system is implemented on Artificial Intelligence (AI) enabled edge GPU. The simulation results show that our proposed framework offers low positioning and trajectory errors, while handling intermittent sensor measurements.

本文为自动驾驶车辆提供了基于激光雷达的同时定位和映射（SLAM）。研究了来自地标传感器的数据和自适应卡尔曼滤波器（KF）中的带状惯性测量单元（IMU）加上系统的可观察性。除了车辆的状态和具有里程碑意义的位置外，自我调整过滤器还估计IMU校准参数以及测量噪声的协方差。流程噪声，状态过渡矩阵和观察灵敏度矩阵的离散时间协方差矩阵以封闭形式得出，使其适合实时实现。检查3D SLAM系统的可观察性得出的结论是，该系统在地标对准的几何条件下仍然可以观察到。