我们提出了一种系统解决方案，以实现使用热图像和惯性测量的飞行机器人团队的数据效率，分散的状态估计。每个机器人可以独立飞行，并在可能的情况下交换数据以完善其状态估计。我们的系统前端应用在线光度校准以完善热图像，从而增强功能跟踪并放置识别。我们的系统后端使用协方差融合策略来忽略代理之间的互相关，以降低内存使用和计算成本。通信管道使用本地汇总的描述符（VLAD）的向量来构建需要较低带宽使用情况的请求响应策略。我们在合成数据和现实世界数据上测试我们的协作方法。我们的结果表明，相对于个人代理方法，该提出的方法最多可提高46％的轨迹估计，同时减少多达89％的通信交换。数据集和代码将发布给公众，扩展了已经发布的JPL XVIO库。

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。

In recent years, aerial swarm technology has developed rapidly. In order to accomplish a fully autonomous aerial swarm, a key technology is decentralized and distributed collaborative SLAM (CSLAM) for aerial swarms, which estimates the relative pose and the consistent global trajectories. In this paper, we propose $D^2$SLAM: a decentralized and distributed ($D^2$) collaborative SLAM algorithm. This algorithm has high local accuracy and global consistency, and the distributed architecture allows it to scale up. $D^2$SLAM covers swarm state estimation in two scenarios: near-field state estimation for high real-time accuracy at close range and far-field state estimation for globally consistent trajectories estimation at the long-range between UAVs. Distributed optimization algorithms are adopted as the backend to achieve the $D^2$ goal. $D^2$SLAM is robust to transient loss of communication, network delays, and other factors. Thanks to the flexible architecture, $D^2$SLAM has the potential of applying in various scenarios.

去中心化的国家估计是GPS贬低的地区自动空中群体系统中最基本的组成部分之一，但它仍然是一个极具挑战性的研究主题。本文提出了Omni-swarm，一种分散的全向视觉惯性-UWB状态估计系统，用于解决这一研究利基市场。为了解决可观察性，复杂的初始化，准确性不足和缺乏全球一致性的问题，我们在Omni-warm中引入了全向感知前端。它由立体宽型摄像机和超宽带传感器，视觉惯性探测器，基于多无人机地图的本地化以及视觉无人机跟踪算法组成。前端的测量值与后端的基于图的优化融合在一起。所提出的方法可实现厘米级的相对状态估计精度，同时确保空中群中的全球一致性，这是实验结果证明的。此外，在没有任何外部设备的情况下，可以在全面的无人机间碰撞方面支持，表明全旋转的潜力是自动空中群的基础。

近年来我们目睹了巨大进展的动机，本文提出了对协作同时定位和映射（C-SLAM）主题的科学文献的调查，也称为多机器人猛击。随着地平线上的自动驾驶车队和工业应用中的多机器人系统的兴起，我们相信合作猛击将很快成为未来机器人应用的基石。在本调查中，我们介绍了C-Slam的基本概念，并呈现了彻底的文献综述。我们还概述了C-Slam在鲁棒性，通信和资源管理方面的主要挑战和限制。我们通过探索该地区目前的趋势和有前途的研究途径得出结论。

我们提供了一种基于因子图优化的多摄像性视觉惯性内径系统，该系统通过同时使用所有相机估计运动，同时保留固定的整体特征预算。我们专注于在挑战环境中的运动跟踪，例如狭窄的走廊，具有侵略性动作的黑暗空间，突然的照明变化。这些方案导致传统的单眼或立体声测量失败。在理论上，使用额外的相机跟踪运动，但它会导致额外的复杂性和计算负担。为了克服这些挑战，我们介绍了两种新的方法来改善多相机特征跟踪。首先，除了从一体相机移动到另一个相机时，我们连续地跟踪特征的代替跟踪特征。这提高了准确性并实现了更紧凑的因子图表示。其次，我们选择跨摄像机的跟踪功能的固定预算，以降低反向结束优化时间。我们发现，使用较小的信息性功能可以保持相同的跟踪精度。我们所提出的方法使用由IMU和四个摄像机（前立体网和两个侧面）组成的硬件同步装置进行广泛测试，包括：地下矿，大型开放空间，以及带狭窄楼梯和走廊的建筑室内设计。与立体声最新的视觉惯性内径测量方法相比，我们的方法将漂移率，相对姿势误差，高达80％的翻译和旋转39％降低。

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 .

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。

With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E.

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。 。

This paper presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models.The first main novelty is a feature-based tightly-integrated visual-inertial SLAM system that fully relies on Maximum-a-Posteriori (MAP) estimation, even during the IMU initialization phase. The result is a system that operates robustly in real time, in small and large, indoor and outdoor environments, and is two to ten times more accurate than previous approaches.The second main novelty is a multiple map system that relies on a new place recognition method with improved recall. Thanks to it, ORB-SLAM3 is able to survive to long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting mapped areas. Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information. This allows to include in bundle adjustment co-visible keyframes, that provide high parallax observations boosting accuracy, even if they are widely separated in time or if they come from a previous mapping session.Our experiments show that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.5 cm in the EuRoC drone and 9 mm under quick hand-held motions in the room of TUM-VI dataset, a setting representative of AR/VR scenarios. For the benefit of the community we make public the source code.

本文介绍了Cerberus机器人系统系统，该系统赢得了DARPA Subterranean挑战最终活动。出席机器人自主权。由于其几何复杂性，降解的感知条件以及缺乏GPS支持，严峻的导航条件和拒绝通信，地下设置使自动操作变得特别要求。为了应对这一挑战，我们开发了Cerberus系统，该系统利用了腿部和飞行机器人的协同作用，再加上可靠的控制，尤其是为了克服危险的地形，多模式和多机器人感知，以在传感器退化，以及在传感器退化的条件下进行映射以及映射通过统一的探索路径计划和本地运动计划，反映机器人特定限制的弹性自主权。 Cerberus基于其探索各种地下环境及其高级指挥和控制的能力，表现出有效的探索，对感兴趣的对象的可靠检测以及准确的映射。在本文中，我们报告了DARPA地下挑战赛的初步奔跑和最终奖项的结果，并讨论了为社区带来利益的教训所面临的亮点和挑战。

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.

在本文中，我们使用单个摄像头和惯性测量单元（IMU）以及相应的感知共识问题（即，所有观察者的独特性和相同的ID）来解决基于视觉的检测和跟踪多个航空车的问题。我们设计了几种基于视觉的分散贝叶斯多跟踪滤波策略，以解决视觉探测器算法获得的传入的未分类测量与跟踪剂之间的关联。我们根据团队中代理的数量在不同的操作条件以及可扩展性中比较它们的准确性。该分析提供了有关给定任务最合适的设计选择的有用见解。我们进一步表明，提出的感知和推理管道包括深度神经网络（DNN），因为视觉目标检测器是轻量级的，并且能够同时运行控制和计划，并在船上进行大小，重量和功率（交换）约束机器人。实验结果表明，在各种具有挑战性的情况（例如重闭）中，有效跟踪了多个无人机。

Integration of multiple sensor modalities and deep learning into Simultaneous Localization And Mapping (SLAM) systems are areas of significant interest in current research. Multi-modality is a stepping stone towards achieving robustness in challenging environments and interoperability of heterogeneous multi-robot systems with varying sensor setups. With maplab 2.0, we provide a versatile open-source platform that facilitates developing, testing, and integrating new modules and features into a fully-fledged SLAM system. Through extensive experiments, we show that maplab 2.0's accuracy is comparable to the state-of-the-art on the HILTI 2021 benchmark. Additionally, we showcase the flexibility of our system with three use cases: i) large-scale (approx. 10 km) multi-robot multi-session (23 missions) mapping, ii) integration of non-visual landmarks, and iii) incorporating a semantic object-based loop closure module into the mapping framework. The code is available open-source at https://github.com/ethz-asl/maplab.

准确的自我和相对状态估计是完成群体任务的关键前提，例如协作自主探索，目标跟踪，搜索和救援。本文提出了一种全面分散的状态估计方法，用于空中群体系统，其中每个无人机执行精确的自我状态估计，通过无线通信交换自我状态和相互观察信息，并估算相对状态（W.R.T.）（W.R.T.）无人机，全部实时，仅基于激光惯性测量。提出了一种基于3D激光雷达的新型无人机检测，识别和跟踪方法，以获得队友无人机的观察。然后，将相互观察测量与IMU和LIDAR测量紧密耦合，以实时和准确地估计自我状态和相对状态。广泛的现实世界实验显示了对复杂场景的广泛适应性，包括被GPS贬低的场景，摄影机的退化场景（漆黑的夜晚）或激光雷达（面对单个墙）。与运动捕获系统提供的地面真相相比，结果显示了厘米级的定位精度，该精度优于单个无人机系统的其他最先进的激光惯性射测。

本文提出了Kimera-Multi，第一个多机器人系统，（i）是强大的，并且能够识别和拒绝由感知混叠产生的不正确和内部机器人循环闭合，（ii）完全分布，仅依赖于本地（点对点）通信实现分布式本地化和映射，（iii）实时构建环境的全球一致的度量标准三维网状模型，其中网格的面部用语义标签注释。 Kimera-Multi由配备有视觉惯性传感器的机器人团队实现。每个机器人都构建了局部轨迹估计和使用Kimera的本地网格。当通信可用时，机器人基于一种基于新型分布式刻度非凸性算法发起分布式地点识别和鲁棒姿态图优化协议。所提出的协议允许机器人通过利用机器人间循环闭合而鲁棒到异常值来改善其局部轨迹估计。最后，每个机器人使用其改进的轨迹估计来使用网格变形技术来校正本地网格。我们在光逼真模拟，SLAM基准测试数据集中展示了Kimera-Multi，以及使用地机器人收集的靠户外数据集。真实和模拟实验都涉及长轨迹（例如，每个机器人高达800米）。实验表明，在鲁棒性和准确性方面，kimera-multi（i）优于现有技术，（ii）在完全分布的同时实现与集中式大满贯系统相当的估计误差，（iii）在通信带宽方面是显着的（iv）产生精确的公制语义3D网格，并且（v）是模块化的，也可以用于标准3D重建（即，没有语义标签）或轨迹估计（即，不重建3D网格）。

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

Precise geolocalization is crucial for unmanned aerial vehicles (UAVs). However, most current deployed UAVs rely on the global navigation satellite systems (GNSS) or high precision inertial navigation systems (INS) for geolocalization. In this paper, we propose to use a lightweight visual-inertial system with a 2D georeference map to obtain accurate and consecutive geodetic positions for UAVs. The proposed system firstly integrates a micro inertial measurement unit (MIMU) and a monocular camera as odometry to consecutively estimate the navigation states and reconstruct the 3D position of the observed visual features in the local world frame. To obtain the geolocation, the visual features tracked by the odometry are further registered to the 2D georeferenced map. While most conventional methods perform image-level aerial image registration, we propose to align the reconstructed points to the map points in the geodetic frame; this helps to filter out the large portion of outliers and decouples the negative effects from the horizontal angles. The registered points are then used to relocalize the vehicle in the geodetic frame. Finally, a pose graph is deployed to fuse the geolocation from the aerial image registration and the local navigation result from the visual-inertial odometry (VIO) to achieve consecutive and drift-free geolocalization performance. We have validated the proposed method by installing the sensors to a UAV body rigidly and have conducted two flights in different environments with unknown initials. The results show that the proposed method can achieve less than 4m position error in flight at 100m high and less than 9m position error in flight about 300m high.

Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in security applications, robust localization algorithms independent of GNSS are needed to provide dependable operations of autonomous UAVs also in interfered conditions. Typical non-GNSS visual localization approaches rely on known starting pose, work only on a small-sized map, or require known flight paths before a mission starts. We consider the problem of localization with no information on initial pose or planned flight path. We propose a solution for global visual localization on a map at scale up to 100 km2, based on matching orthoprojected UAV images to satellite imagery using learned season-invariant descriptors. We show that the method is able to determine heading, latitude and longitude of the UAV at 12.6-18.7 m lateral translation error in as few as 23.2-44.4 updates from an uninformed initialization, also in situations of significant seasonal appearance difference (winter-summer) between the UAV image and the map. We evaluate the characteristics of multiple neural network architectures for generating the descriptors, and likelihood estimation methods that are able to provide fast convergence and low localization error. We also evaluate the operation of the algorithm using real UAV data and evaluate running time on a real-time embedded platform. We believe this is the first work that is able to recover the pose of an UAV at this scale and rate of convergence, while allowing significant seasonal difference between camera observations and map.