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。

精确和实时轨道车辆本地化以及铁路环境监测对于铁路安全至关重要。在这封信中，我们提出了一种基于多激光器的同时定位和映射（SLAM）系统，用于铁路应用。我们的方法从测量开始预处理，以便去噪并同步多个LIDAR输入。根据LIDAR放置使用不同的帧到框架注册方法。此外，我们利用来自提取的轨道轨道的平面约束来提高系统精度。本地地图进一步与利用绝对位置测量的全局地图对齐。考虑到不可避免的金属磨损和螺杆松动，在手术期间唤醒了在线外在细化。在收集3000公里的数据集上广泛验证了所提出的方法。结果表明，所提出的系统与大规模环境的有效映射一起实现了精确且稳健的本地化。我们的系统已应用于运费交通铁路以监控任务。

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。

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.

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 .

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.

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。

传统的LIDAR射测（LO）系统主要利用从经过的环境获得的几何信息来注册激光扫描并估算Lidar Ego-Motion，而在动态或非结构化环境中可能不可靠。本文提出了Inten-loam，一种低饮用和健壮的激光镜和映射方法，该方法完全利用激光扫描的隐式信息（即几何，强度和时间特征）。扫描点被投影到圆柱形图像上，这些图像有助于促进各种特征的有效和适应性提取，即地面，梁，立面和反射器。我们提出了一种新型基于强度的点登记算法，并将其纳入LIDAR的探光仪，从而使LO系统能够使用几何和强度特征点共同估计LIDAR EGO-MOTION。为了消除动态对象的干扰，我们提出了一种基于时间的动态对象删除方法，以在MAP更新之前过滤它们。此外，使用与时间相关的体素网格滤波器组织并缩减了本地地图，以维持当前扫描和静态局部图之间的相似性。在模拟和实际数据集上进行了广泛的实验。结果表明，所提出的方法在正常驾驶方案中实现了类似或更高的精度W.R.T，在非结构化环境中，最先进的方法优于基于几何的LO。

同时定位和映射（SLAM）对于自主机器人（例如自动驾驶汽车，自动无人机），3D映射系统和AR/VR应用至关重要。这项工作提出了一个新颖的LIDAR惯性 - 视觉融合框架，称为R $^3 $ LIVE ++，以实现强大而准确的状态估计，同时可以随时重建光线体图。 R $^3 $ LIVE ++由LIDAR惯性探针（LIO）和视觉惯性探测器（VIO）组成，均为实时运行。 LIO子系统利用从激光雷达的测量值重建几何结构（即3D点的位置），而VIO子系统同时从输入图像中同时恢复了几何结构的辐射信息。 r $^3 $ live ++是基于r $^3 $ live开发的，并通过考虑相机光度校准（例如，非线性响应功能和镜头渐滴）和相机的在线估计，进一步提高了本地化和映射的准确性和映射接触时间。我们对公共和私人数据集进行了更广泛的实验，以将我们提出的系统与其他最先进的SLAM系统进行比较。定量和定性结果表明，我们所提出的系统在准确性和鲁棒性方面对其他系统具有显着改善。此外，为了证明我们的工作的可扩展性，{我们基于重建的辐射图开发了多个应用程序，例如高动态范围（HDR）成像，虚拟环境探索和3D视频游戏。}最后，分享我们的发现和我们的发现和为社区做出贡献，我们在GitHub上公开提供代码，硬件设计和数据集：github.com/hku-mars/r3live

Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). The visual odometry-based method can provide accurate positioning in a short period but is subjected to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected in the UAV indoor and UGV outdoor environments.

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.

事件摄像机是运动激活的传感器，可捕获像素级照明的变化，而不是具有固定帧速率的强度图像。与标准摄像机相比，它可以在高速运动和高动态范围场景中提供可靠的视觉感知。但是，当相机和场景之间的相对运动受到限制时，例如在静态状态下，事件摄像机仅输出一点信息甚至噪音。尽管标准相机可以在大多数情况下，尤其是在良好的照明条件下提供丰富的感知信息。这两个相机完全是互补的。在本文中，我们提出了一种具有鲁棒性，高智能和实时优化的基于事件的视觉惯性镜（VIO）方法，具有事件角度，基于线的事件功能和基于点的图像功能。提出的方法旨在利用人为场景中的自然场景和基于线路的功能中的基于点的功能，以通过设计良好设计的功能管理提供更多其他结构或约束信息。公共基准数据集中的实验表明，与基于图像或基于事件的VIO相比，我们的方法可以实现卓越的性能。最后，我们使用我们的方法演示了机上闭环自动驾驶四极管飞行和大规模室外实验。评估的视频在我们的项目网站上介绍：https：//b23.tv/oe3qm6j

尽管数十年来，同时定位和映射（SLAM）一直是一个积极的研究主题，但由于特征不足或其固有的估计漂移，在许多平民环境中，当前的最新方法仍然遭受不稳定或不准确性的困扰。为了解决这些问题，我们提出了一个梳理SLAM和先前基于图的本地化的导航系统。具体而言，我们考虑了线条和平面特征的其他集成，这些特征在平民环境中无处不在，在结构上更突出，以确保功能充足和本地化的鲁棒性。更重要的是，我们将一般的先验地图信息纳入SLAM以限制其漂移并提高准确性。为了避免在先前的信息和局部观察之间进行严格的关联，我们将先验知识的参数化为低维结构先验，定义为不同几何原始原始人之间的相对距离/角度。本地化被公式化为基于图的优化问题，其中包含基于滑动窗口的变量和因素，包括IMU，异质特征和结构先验。我们还得出了不同因素的雅各布人的分析表达式，以避免自动分化开销。为了进一步减轻结合结构先验因素的计算负担，根据所谓的信息增益采用了选择机制，以仅将最有效的结构先验纳入图表优化中。最后，对综合数据，公共数据集以及更重要的是，对所提出的框架进行了广泛的测试。结果表明，所提出的方案可以有效地提高平民应用中自动驾驶机器人的本地化的准确性和鲁棒性。

同时定位和映射（SLAM）被认为是智能车辆和移动机器人的重要功能。但是，当前的大多数LiDAR SLAM方法都是基于静态环境的假设。因此，在具有多个移动对象的动态环境中的本地化实际上是不可靠的。本文提出了一个动态的SLAM框架RF-LIO，该框架在LIO-SAM上构建，该框架添加了自适应多分辨率范围图像，并使用紧密耦合的LIDAR惯性探测器首先删除移动对象，然后将激光镜扫描与子束相匹配。因此，即使在高动态环境中，它也可以获得准确的姿势。在自收集的数据集和Open UrbanLoco数据集上评估了提出的RF-LIO。高动态环境中的实验结果表明，与壤土和LIO-SAM相比，所提出的RF-LIO的绝对轨迹精度分别可以提高90％和70％。 RF-LIO是高动态环境中最先进的大满贯系统之一。

本文提出了一种新颖的方法，用于在具有复杂拓扑结构的地下领域的搜索和救援行动中自动合作。作为CTU-Cras-Norlab团队的一部分，拟议的系统在DARPA SubT决赛的虚拟轨道中排名第二。与专门为虚拟轨道开发的获奖解决方案相反，该建议的解决方案也被证明是在现实世界竞争极为严峻和狭窄的环境中飞行的机上实体无人机的强大系统。提出的方法可以使无缝模拟转移的无人机团队完全自主和分散的部署，并证明了其优于不同环境可飞行空间的移动UGV团队的优势。该论文的主要贡献存在于映射和导航管道中。映射方法采用新颖的地图表示形式 - 用于有效的风险意识长距离计划，面向覆盖范围和压缩的拓扑范围的LTVMAP领域，以允许在低频道通信下进行多机器人合作。这些表示形式与新的方法一起在导航中使用，以在一般的3D环境中可见性受限的知情搜索，而对环境结构没有任何假设，同时将深度探索与传感器覆盖的剥削保持平衡。所提出的解决方案还包括一条视觉感知管道，用于在没有专用GPU的情况下在5 Hz处进行四个RGB流中感兴趣的对象的板上检测和定位。除了参与DARPA SubT外，在定性和定量评估的各种环境中，在不同的环境中进行了广泛的实验验证，UAV系统的性能得到了支持。

在过去的几十年，光探测和测距（LIDAR）技术已被广泛研究作为自我定位与地图强大的替代方案。这些典型地接近状态自运动估计作为非线性优化问题取决于当前点云和地图之间建立的对应关系，无论其范围，局部或全局的。本文提出LiODOM，对于姿态估计和地图建设的新的激光雷达仅里程计和绘图方法中，基于最小化从一组加权点 - 线对应的衍生与本地地图损失函数从该组可用的抽象点云。此外，该工作场所特别强调赋予其快速数据关联的相关地图表示。为了有效地代表了环境，我们提出了一个数据结构与哈希方案相结合，可以快速进入地图的任何部分。 LiODOM通过在公共数据集的一组实验中，对于其媲美针对其它解决方案的装置验证。它的性能上，主板还报告了一个空中平台。

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).

We propose a real-time method for odometry and mapping using range measurements from a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation can cause mis-registration of the resulting point cloud. To date, coherent 3D maps can be built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements.The key idea in obtaining this level of performance is the division of the complex problem of simultaneous localization and mapping, which seeks to optimize a large number of variables simultaneously, by two algorithms. One algorithm performs odometry at a high frequency but low fidelity to estimate velocity of the lidar. Another algorithm runs at a frequency of an order of magnitude lower for fine matching and registration of the point cloud. Combination of the two algorithms allows the method to map in real-time. The method has been evaluated by a large set of experiments as well as on the KITTI odometry benchmark. The results indicate that the method can achieve accuracy at the level of state of the art offline batch methods.

Accurate and consistent vehicle localization in urban areas is challenging due to the large-scale and complicated environments. In this paper, we propose onlineFGO, a novel time-centric graph-optimization-based localization method that fuses multiple sensor measurements with the continuous-time trajectory representation for vehicle localization tasks. We generalize the graph construction independent of any spatial sensor measurements by creating the states deterministically on time. As the trajectory representation in continuous-time enables querying states at arbitrary times, incoming sensor measurements can be factorized on the graph without requiring state alignment. We integrate different GNSS observations: pseudorange, deltarange, and time-differenced carrier phase (TDCP) to ensure global reference and fuse the relative motion from a LiDAR-odometry to improve the localization consistency while GNSS observations are not available. Experiments on general performance, effects of different factors, and hyper-parameter settings are conducted in a real-world measurement campaign in Aachen city that contains different urban scenarios. Our results show an average 2D error of 0.99m and consistent state estimation in urban scenarios.