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).

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.

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

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。

在本文中，我们评估了八种流行和开源的3D激光雷达和视觉大满贯（同时定位和映射）算法，即壤土，乐高壤土，lio sam，hdl graph，orb slam3，basalt vio和svo2。我们已经设计了室内和室外的实验，以研究以下项目的影响：i）传感器安装位置的影响，ii）地形类型和振动的影响，iii）运动的影响（线性和角速速度的变化）。我们根据相对和绝对姿势误差比较它们的性能。我们还提供了他们所需的计算资源的比较。我们通过我们的多摄像机和多大摄像机室内和室外数据集进行彻底分析和讨论结果，并确定环境案例的最佳性能系统。我们希望我们的发现可以帮助人们根据目标环境选择一个适合其需求的传感器和相应的SLAM算法组合。

在本文中，我们介绍了全球导航卫星系统（GNSS）辅助激光乐队 - 视觉惯性方案RAILTOMER-V，用于准确且坚固的铁路车辆本地化和映射。 Raillomer-V在因子图上制定，由两个子系统组成：辅助LiDar惯性系统（OLIS）和距离的内径综合视觉惯性系统（OVI）。两个子系统都利用了铁路上的典型几何结构。提取的轨道轨道的平面约束用于补充OLI中的旋转和垂直误差。此外，线特征和消失点被利用以限制卵巢中的旋转漂移。拟议的框架在800公里的数据集中广泛评估，聚集在一年以上的一般速度和高速铁路，日夜。利用各个传感器的所有测量的紧密耦合集成，我们的框架准确到了长期的任务，并且足够强大地避免了退行的情景（铁路隧道）。此外，可以使用车载计算机实现实时性能。

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。

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。

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 .

The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for unmanned ground vehicles, which is appropriate for versatile search and rescue applications. Compared with existing LiDAR-based localization and mapping systems such as LOAM, we have two major contributions: the first is the improvement of the robustness of particle swarm filter-based LiDAR SLAM, while the second is the loop closure methods developed for global optimization to improve the localization accuracy of the whole system. We demonstrate by experiments that the accuracy and robustness of the LiDAR SLAM system are both improved. Finally, we have done systematic experimental tests at the Hong Kong science park as well as other indoor or outdoor real complicated testing circumstances, which demonstrates the effectiveness and efficiency of our approach. It is demonstrated that our system has high accuracy, robustness, as well as efficiency. Our system is of great importance to the localization and mapping of the unmanned ground vehicle in an unknown environment.

本文通过讨论参加了为期三年的SubT竞赛的六支球队的不同大满贯策略和成果，报道了地下大满贯的现状。特别是，本文有四个主要目标。首先，我们审查团队采用的算法，架构和系统；特别重点是以激光雷达以激光雷达为中心的SLAM解决方案（几乎所有竞争中所有团队的首选方法），异质的多机器人操作（包括空中机器人和地面机器人）和现实世界的地下操作（从存在需要处理严格的计算约束的晦涩之处）。我们不会回避讨论不同SubT SLAM系统背后的肮脏细节，这些系统通常会从技术论文中省略。其次，我们通过强调当前的SLAM系统的可能性以及我们认为与一些良好的系统工程有关的范围来讨论该领域的成熟度。第三，我们概述了我们认为是基本的开放问题，这些问题可能需要进一步的研究才能突破。最后，我们提供了在SubT挑战和相关工作期间生产的开源SLAM实现和数据集的列表，并构成了研究人员和从业人员的有用资源。

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odometry solution is used to estimate the bias of the IMU. To ensure high performance in real-time, we marginalize old lidar scans for pose optimization, rather than matching lidar scans to a global map. Scan-matching at a local scale instead of a global scale significantly improves the real-time performance of the system, as does the selective introduction of keyframes, and an efficient sliding window approach that registers a new keyframe to a fixed-size set of prior "sub-keyframes." The proposed method is extensively evaluated on datasets gathered from three platforms over various scales and environments.

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。

我们提出了一种准确而坚固的多模态传感器融合框架，Metroloc，朝着最极端的场景之一，大规模地铁车辆本地化和映射。 Metroloc在以IMU为中心的状态估计器上构建，以较轻耦合的方法紧密地耦合光检测和测距（LIDAR），视觉和惯性信息。所提出的框架由三个子模块组成：IMU Odometry，LiDar - 惯性内径术（LIO）和视觉惯性内径（VIO）。 IMU被视为主要传感器，从LIO和VIO实现了从LIO和VIO的观察，以限制加速度计和陀螺仪偏差。与以前的点LIO方法相比，我们的方法通过将线路和平面特征引入运动估计来利用更多几何信息。 VIO还通过使用两条线和点来利用环境结构信息。我们所提出的方法在具有维护车辆的长期地铁环境中广泛测试。实验结果表明，该系统比使用实时性能的最先进的方法更准确和强大。此外，我们开发了一系列虚拟现实（VR）应用，以实现高效，经济，互动的轨道车辆状态和轨道基础设施监控，已经部署到室外测试铁路。

农业行业不断寻求农业生产中涉及的不同过程的自动化，例如播种，收获和杂草控制。使用移动自主机器人执行这些任务引起了极大的兴趣。耕地面向同时定位和映射（SLAM）系统（移动机器人技术的关键）面临着艰巨的挑战，这是由于视觉上的难度，这是由于高度重复的场景而引起的。近年来，已经开发了几种视觉惯性遗传（VIO）和SLAM系统。事实证明，它们在室内和室外城市环境中具有很高的准确性。但是，在农业领域未正确评估它们。在这项工作中，我们从可耕地上的准确性和处理时间方面评估了最相关的最新VIO系统，以便更好地了解它们在这些环境中的行为。特别是，该评估是在我们的车轮机器人记录的大豆领域记录的传感器数据集中进行的，该田间被公开发行为Rosario数据集。评估表明，环境的高度重复性外观，崎terrain的地形产生的强振动以及由风引起的叶子的运动，暴露了当前最新的VIO和SLAM系统的局限性。我们分析了系统故障并突出观察到的缺点，包括初始化故障，跟踪损失和对IMU饱和的敏感性。最后，我们得出的结论是，即使某些系统（例如Orb-Slam3和S-MSCKF）在其他系统方面表现出良好的结果，但应采取更多改进，以使其在某些申请中的农业领域可靠，例如作物行的土壤耕作和农药喷涂。 。

在本文中，我们提出了一个与RGB，深度，IMU和结构化平面信息融合的紧密耦合的大满贯系统。传统的基于稀疏点的大满贯系统始终保持大量地图点以建模环境。大量的地图点使我们具有很高的计算复杂性，因此很难在移动设备上部署。另一方面，平面是人造环境中的常见结构，尤其是在室内环境中。我们通常可以使用少量飞机代表大型场景。因此，本文的主要目的是降低基于稀疏点的大满贯的高复杂性。我们构建了一个轻巧的后端地图，该地图由几个平面和地图点组成，以相等或更高的精度实现有效的捆绑捆绑调整（BA）。我们使用统计约束来消除优化中众多平面点的参数，并降低BA的复杂性。我们将同构和点对平面约束的参数和测量分开，并压缩测量部分，以进一步有效地提高BA的速度。我们还将平面信息集成到整个系统中，以实现强大的平面特征提取，数据关联和全球一致的平面重建。最后，我们进行消融研究，并用模拟和真实环境数据中的类似方法比较我们的方法。我们的系统在准确性和效率方面具有明显的优势。即使平面参数参与了优化，我们也可以使用平面结构有效地简化后端图。全局捆绑捆绑调整的速度几乎是基于稀疏点的SLAM算法的2倍。

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.

This article proposes a method to diminish the pose (position plus attitude) drift experienced by an SVO (Semi-Direct Visual Odometry) based visual navigation system installed onboard a UAV (Unmanned Air Vehicle) by supplementing its pose estimation non linear optimizations with priors based on the outputs of a GNSS (Global Navigation Satellite System) Denied inertial navigation system. The method is inspired in a PI (Proportional Integral) control system, in which the attitude, altitude, and rate of climb inertial outputs act as targets to ensure that the visual estimations do not deviate far from their inertial counterparts. The resulting IA-VNS (Inertially Assisted Visual Navigation System) achieves major reductions in the horizontal position drift inherent to the GNSS-Denied navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs. Additionally, the IA-VNS can be considered as a virtual incremental position (ground velocity) sensor capable of providing observations to the inertial filter. Stochastic high fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results and to analyze their sensitivity to the terrain type overflown by the aircraft as well as to the quality of the onboard sensors on which the priors are based. The author releases the C ++ implementation of both the navigation algorithms and the high fidelity simulation as open-source software.

交付机器人旨在获得高精度以促进完全自主权。需要一个精确的人行行周围环境的三维点云图来估计自定位。有或没有循环结束方法，由于传感器漂移，较大的城市或城市地图映射后累积误差会逐渐增加。因此，使用漂移或错位的地图存在很高的风险。本文提出了一种融合GPS更新3D点云并消除累积错误的技术。提出的方法与其他现有方法显示了定量比较和定性评估的出色结果。

对深度神经网络（DNN）进行了训练，以估计在城市区域驾驶的汽车速度，并输入来自低成本六轴惯性测量单元（IMU）的测量流。通过在配备了全球导航卫星系统（GNSS）实时运动学（RTK）定位设备和同步IMU的汽车中，通过驾驶以色列阿什杜德市（Ashdod）驾驶以色列市Ashdod市收集了三个小时的数据。使用以50 Hz的高速率获得的位置测量值计算了汽车速度的地面真实标签。提出了具有长短期内存层的DNN体系结构，以实现高频速度估计，以说明以前的输入历史记录和速度，加速度和角速度之间的非线性关系。制定了简化的死亡算法定位方案，以评估训练有素的模型，该模型提供了速度伪测量。训练有素的模型显示可在4分钟车程中大大提高位置准确性，而无需使用GNSS位置更新。