近年来,自动路滚轮作为一种流行的建筑机器人,吸引了行业和研究界的兴趣。然而,当涉及突破退变问题的隧道时,为机器人提供准确的定位结果,仍然是一个具有挑战性的问题。在本文中,我们的目的是通过基于优化来解决激光雷达和UWB测量来处理这个问题。在所提出的定位方法中,将受到限制的非变性的指示,将引入UWB重建的协方差以提高本地化的准确性。除此之外,还介绍了一种可以提取隧道内壁的特征以辅助定位的方法。为了评估所提出的方法的有效性,进行了真正的公路滚轮的三个实验,结果表明,我们的方法可以实现比现有方法更好的性能,并且可以应用于隧道内部工作的自动路滚轮。最后,我们讨论了在实际应用中部署系统的可行性,并提出了一些建议。
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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。
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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。
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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。
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准确可靠的传感器校准对于在自主驾驶中融合激光雷达和惯性测量至关重要。本文提出了一种新型的3D-LIDAR和姿势传感器的新型三阶段外部校准方法,用于自主驾驶。第一阶段可以通过点云表面特征快速校准传感器之间的外部参数,以便可以将外部参数从大的初始误差范围缩小到很小的时间范围。第二阶段可以基于激光映射空间占用率进一步校准外部参数,同时消除运动失真。在最后阶段,校正了由自动驾驶汽车的平面运动引起的Z轴误差,并最终获得了精确的外部参数。具体而言,该方法利用了道路场景的自然特征,使其独立且易于在大规模条件下应用。现实世界数据集的实验结果证明了我们方法的可靠性和准确性。这些代码是在GitHub网站上开源的。据我们所知,这是第一个专门为自动驾驶设计的开源代码,用于校准激光雷达和姿势传感器外部参数。代码链接是https://github.com/opencalib/lidar2ins。
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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。
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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。
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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.
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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
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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。
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通过移动激光扫描和图像构建有色点的云是测量和映射的基本工作。它也是为智能城市建造数字双胞胎的重要先决条件。但是,现有的公共数据集要么是相对较小的规模,要么缺乏准确的几何和彩色地面真理。本文记录了一个名为Polyu-BPComa的多功能数据集,该数据集可独特地定位于移动着色映射。该数据集在背包平台上包含3D激光雷达,球形成像,GNSS和IMU的资源。颜色检查器板在每个调查区域粘贴,因为目标和地面真相数据是由先进的陆地激光扫描仪(TLS)收集的。 3D几何信息和颜色信息可以分别在背包系统和TLS产生的有色点云中恢复。因此,我们提供了一个机会,可以同时为移动多感官系统对映射和着色精度进行基准测试。该数据集的尺寸约为800 GB,涵盖室内和室外环境。数据集和开发套件可在https://github.com/chenpengxin/polyu-bpcoma.git上找到。
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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。
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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。
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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.
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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.
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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.
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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.
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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.
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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。
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姿势估计准确性的提高是目前移动机器人中的基本问题。本研究旨在改善观察的使用以提高准确性。选择要点的选择会影响姿势估计的准确性,导致观察贡献如何影响系统的问题。因此,分析了信息对姿势估计过程的贡献。此外,配制了不确定性模型,灵敏度模型和贡献理论,提供了一种计算每种残留项的贡献的方法。所提出的选择方法已经理解证明能够实现全局统计最优。所提出的方法在人工数据模拟上进行测试,与基特基准进行比较。该实验揭示了与Aloam和Mloam对比的优异结果。所提出的算法在LIDAR Idomatry和LIDAR惯性内径术中使用不同的LIDAR传感器,使用不同的扫描模式,展示其提高姿态估计精度的有效性。随后推断出两个激光扫描传感器的新配置。该配置对于先前地图中的三维姿态定位是有效的,并且产生厘米级的结果。
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