Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the environment contains structureless areas such as long hallways. To allow the use of lidar-based SLAM in such environments, we propose to add reflector markers in specific locations that would otherwise be difficult. We present an algorithm to reliably detect these markers and two approaches to fuse the detected markers with geometry-based scan matching. The performance of the proposed methods is demonstrated on real-world datasets from several industrial environments.

我们提出了BioSlam，这是一个终生的SLAM框架，用于逐步学习各种新出现，并在先前访问的地区保持准确的位置识别。与人类不同，人工神经网络遭受灾难性遗忘的困扰，并在接受新来者训练时可能会忘记先前访问的地区。对于人类而言，研究人员发现，大脑中存在一种记忆重播机制，可以使神经元保持活跃。受到这一发现的启发，Bioslam设计了一个封闭式的生成重播，以根据反馈奖励来控制机器人的学习行为。具体而言，BioSlam提供了一种新型的双记忆机制来维护：1）动态记忆有效地学习新观察结果，以及2）平衡新老知识的静态记忆。当与基于视觉/激光雷达的SLAM系统结合使用时，完整的处理管道可以帮助代理逐步更新位置识别能力，从而强大，从而增强长期位置识别的复杂性。我们在两个渐进式猛击场景中展示了Bioslam。在第一种情况下，基于激光雷达的特工不断穿越具有120公里轨迹的城市尺度环境，并遇到了不同类型的3D几何形状（开放街，住宅区，商业建筑）。我们表明，BioSlam可以逐步更新代理商的位置识别能力，并优于最先进的增量方法，即生成重播24％。在第二种情况下，基于激光镜的代理商在4.5公里的轨迹上反复穿越校园规模区域。 Bioslam可以保证在不同外观下的最先进方法上优于15％的地方识别精度。据我们所知，BioSlam是第一个具有记忆力增强的终身大满贯系统，可以帮助长期导航任务中的逐步识别。

我们提出Automerge，这是一种LIDAR数据处理框架，用于将大量地图段组装到完整的地图中。传统的大规模地图合并方法对于错误的数据关联是脆弱的，并且主要仅限于离线工作。 Automerge利用多观点的融合和自适应环路闭合检测来进行准确的数据关联，并且它使用增量合并来从随机顺序给出的单个轨迹段组装大图，没有初始估计。此外，在组装段后，自动制度可以执行良好的匹配和姿势图片优化，以在全球范围内平滑合并的地图。我们展示了城市规模合并（120公里）和校园规模重复合并（4.5公里x 8）的汽车。该实验表明，自动化（i）在段检索中超过了第二和第三最佳方法的14％和24％的召回，（ii）在120 km大尺度地图组件（III）中实现了可比较的3D映射精度，IT对于暂时的重新审视是强大的。据我们所知，Automerge是第一种映射方法，它可以在无GPS的帮助下合并数百公里的单个细分市场。

对于长期自治，大多数位置识别方法主要在简化的方案或模拟数据集上进行评估，该数据集无法提供可靠的证据来评估当前同时定位和映射的准备就绪（SLAM）。在本文中，我们提出了一个长期的位置识别数据集，用于在大规模动态环境下用于移动定位。该数据集包括一个校园规模的轨道和城市规模的轨道：1）校园轨道重点关注长期财产，我们在10个轨迹上记录Lidar设备和一个全向相机，并且每个轨迹在变体下重复记录8次照明条件。 2）城市轨道聚焦大型物业，我们将激光雷达设备安装在车辆上，并穿过120公里种类在城市环境中。每个轨迹都提供了两个轨道的地面真实位置，这是从全球位置系统中获得的，具有额外的基于ICP的点云的细化。为了简化评估程序，我们还为Python-API提供了一组地点识别指标，以快速加载我们的数据集并根据不同方法评估识别性能。该数据集的目标是寻找具有高位置识别精度和鲁棒性的方法，并提供长期自治的真正机器人系统。可以从https://github.com/metaslam/alita访问数据集和提供的工具。

Enabling vertical take-off and landing while providing the ability to fly long ranges opens the door to a wide range of new real-world aircraft applications while improving many existing tasks. Tiltrotor vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are a better choice than fixed-wing and multirotor aircraft for such applications. Prior works on these aircraft have addressed aerodynamic performance, design, modeling, and control. However, a less explored area is the study of their potential fault tolerance due to their inherent redundancy, which allows them to tolerate some degree of actuation failure. This paper introduces tolerance to several types of actuator failures in a tiltrotor VTOL aircraft. We discuss the design and modeling of a custom tiltrotor VTOL UAV, which is a combination of a fixed-wing aircraft and a quadrotor with tilting rotors, where the four propellers can be rotated individually. Then, we analyze the feasible wrench space the vehicle can generate and design the dynamic control allocation so that the system can adapt to actuator failures, benefiting from the configuration redundancy. The proposed approach is lightweight and is implemented as an extension to an already-existing flight control stack. Extensive experiments validate that the system can maintain the controlled flight under different actuator failures. To the best of our knowledge, this work is the first study of the tiltrotor VTOL's fault-tolerance that exploits the configuration redundancy. The source code and simulation can be accessed at https://theairlab.org/vtol.

对象编码和识别对于自主探索，语义场景理解和重新定位等机器人任务至关重要。以前的方法已经尝试了对象或生成用于对象标识的描述符。然而，这种系统仅限于单个视点的“固定”部分对象表示。在机器人探索设置中，由于机器人从多个视点观察对象，因此需要暂时“不断发展”的全局对象表示。此外，鉴于现实世界中未知新颖对象的广泛分布，对象识别过程必须是类无话的。在此上下文中，我们提出了一种新的时间3D对象编码方法，被称为AirObject，以获取基于对象的全局关键点图形的嵌入。具体地，使用跨从基于曲线图的编码方法获得的多个帧的结构信息的时间卷积网络生成全局3D对象嵌入。我们证明AirObject实现了视频对象识别的最先进的性能，并且对严重的遮挡，感知锯齿，视点换档，变形和缩放变换，表现出最先进的单帧和稳健顺序描述符。据我们所知，AirObject是第一个时间对象编码方法之一。

对象编码和识别对于许多机器人任务是至关重要的，例如自主探索和语义重建。现有的作品依赖于检测到的对象的跟踪，但难以准确调用重新审议的对象。在本文中，我们提出了一种新的对象编码方法，基于关键点的图表，该方法被命名为AirCode。为了强大到检测到的关键点的数量，我们提出了一个特征稀疏编码和对象密度编码方法，以确保每个关键点只能影响对象描述符的一小部分，导致对视点变化具有鲁棒性，缩放，闭塞，甚至物体变形。在实验中，我们表明它实现了比最先进的算法的对象识别的卓越性能，并且能够提供可靠的语义重定位化。它是一个即插即用模块，我们希望它将在各种应用中发挥重要作用。

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided.

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.