机器人相关学术速递[9.3]

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cs.RO机器人相关，共计14篇

【1】 Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation 标题：从离线数据和众包注释中学习受语言制约的机器人行为 链接：https://arxiv.org/abs/2109.01115

作者：Suraj Nair,Eric Mitchell,Kevin Chen,Brian Ichter,Silvio Savarese,Chelsea Finn 机构：Stanford University,Robotics at Google 备注：23 Pages, 18 Figures 摘要：我们研究了从机器人交互的大型离线数据集中学习一系列基于视觉的操作任务的问题。为了实现这一点，人类需要简单有效的方法来为机器人指定任务。目标图像是任务规范的一种流行形式，因为它们已经在机器人的观察空间中扎根。然而，目标图像也有一些缺点：它们不便于人类提供，它们可以过度指定期望的行为，导致稀疏的奖励信号，或者在非目标达成任务的情况下，对任务信息的指定不足。自然语言为任务规范提供了一种方便灵活的替代方法，但同时也带来了机器人观察空间中基础语言的挑战。为了可扩展地了解这一基础，我们建议利用离线机器人数据集（包括高度次优、自主收集的数据）和众包自然语言标签。利用这些数据，我们学习了一个简单的分类器，它可以预测状态的变化是否完成了语言指令。这提供了语言条件奖励功能，可用于离线多任务RL。在我们的实验中，我们发现，在语言条件下的操作任务上，我们的方法比目标图像规范和语言条件下的模仿技术都要好25%以上，并且能够在Franka Emika熊猫机器人上执行自然语言的视觉运动任务，例如“打开右抽屉”和“移动订书机”。 摘要：We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and effective ways of specifying tasks to the robot. Goal images are one popular form of task specification, as they are already grounded in the robot's observation space. However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks. Natural language provides a convenient and flexible alternative for task specification, but comes with the challenge of grounding language in the robot's observation space. To scalably learn this grounding we propose to leverage offline robot datasets (including highly sub-optimal, autonomously collected data) with crowd-sourced natural language labels. With this data, we learn a simple classifier which predicts if a change in state completes a language instruction. This provides a language-conditioned reward function that can then be used for offline multi-task RL. In our experiments, we find that on language-conditioned manipulation tasks our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%, and is able to perform visuomotor tasks from natural language, such as "open the right drawer" and "move the stapler", on a Franka Emika Panda robot.

【2】 The Functional Correspondence Problem 标题：函数对应问题 链接：https://arxiv.org/abs/2109.01097

作者：Zihang Lai,Senthil Purushwalkam,Abhinav Gupta 机构：Carnegie Mellon University 备注：Accepted to ICCV 2021 摘要：在视觉数据中找到对应关系的能力是大多数计算机视觉任务的本质。但是什么是正确的通信？对于同一对象实例的两个不同图像，定义了视觉对应任务。对于属于同一类别的两幅物体图像，在大多数情况下，视觉对应关系的定义相当明确。但是，两个完全不同类别的物体之间的对应关系呢？例如，一只鞋和一个瓶子？有没有信件？灵感来自于人类的能力：（a）超越语义范畴的概括(b） 在推导函数启示的基础上，本文引入了函数对应问题。给定两个对象的图像，我们会问一个简单的问题：对于给定的任务，这两个图像之间的对应关系是什么？例如，瓶子和鞋子之间的对应关系是什么。我们引入了一个新的数据集：FunKPoint，它具有10个任务和20个对象类别的基本事实对应关系。我们还介绍了一个模块化的任务驱动表示法来解决这个问题，并证明了我们学习的表示法对这个任务是有效的。但最重要的是，由于我们的监控信号不受语义的约束，我们证明了我们的学习表示可以更好地推广到少数镜头分类问题上。我们希望这篇论文将启发我们的社区超越语义，更多地关注机器人任务的跨类别泛化和学习表示。 摘要：The ability to find correspondences in visual data is the essence of most computer vision tasks. But what are the right correspondences? The task of visual correspondence is well defined for two different images of same object instance. In case of two images of objects belonging to same category, visual correspondence is reasonably well-defined in most cases. But what about correspondence between two objects of completely different category -- e.g., a shoe and a bottle? Does there exist any correspondence? Inspired by humans' ability to: (a) generalize beyond semantic categories and; (b) infer functional affordances, we introduce the problem of functional correspondences in this paper. Given images of two objects, we ask a simple question: what is the set of correspondences between these two images for a given task? For example, what are the correspondences between a bottle and shoe for the task of pounding or the task of pouring. We introduce a new dataset: FunKPoint that has ground truth correspondences for 10 tasks and 20 object categories. We also introduce a modular task-driven representation for attacking this problem and demonstrate that our learned representation is effective for this task. But most importantly, because our supervision signal is not bound by semantics, we show that our learned representation can generalize better on few-shot classification problem. We hope this paper will inspire our community to think beyond semantics and focus more on cross-category generalization and learning representations for robotics tasks.

【3】 Collision avoidance for multiple MAVs using fast centralized NMPC 标题：使用快速集中式NMPC实现多个MAV的冲突避免 链接：https://arxiv.org/abs/2109.01012

作者：Björn Lindqvist,Sina Sharif Mansouri,Pantelis Sopasakis,George Nikolakopoulos 机构：∗ Robotics Team, Department of Computer, Electrical and Space, Engineering, Lule˚a University of Technology, Lule˚a SE-, Sweden., ∗∗ School of Electronics, Electrical Engineering and Computer Science 备注：None 摘要：本文提出了一种新的控制结构，采用集中非线性模型预测控制（CNMPC）方案来控制多个微型飞行器（MAV）。该控制体系结构使用增强状态系统来控制多个代理，并执行障碍和碰撞避免。所使用的优化算法是开放的，基于近似平均牛顿型最优控制方法（PANOC），该方法为非凸优化问题提供了快速收敛性。目标是为每个单独的代理执行位置参考跟踪，而非线性约束保证了碰撞避免和平滑的控制信号。为了生成满足所有约束的轨迹，对非线性约束采用惩罚方法。通过仿真结果和比较，在计算时间和违反约束方面，以及在代理数量方面，成功地证明了这种新型控制方案的有效性。 摘要：This article proposes a novel control architecture using a centralized nonlinear model predictive control (CNMPC) scheme for controlling multiple micro aerial vehicles (MAVs). The control architecture uses an augmented state system to control multiple agents and performs both obstacle and collision avoidance. The optimization algorithm used is OpEn, based on the proximal averaged Newton type method for optimal control (PANOC) which provides fast convergence for non-convex optimization problems. The objective is to perform position reference tracking for each individual agent, while nonlinear constrains guarantee collision avoidance and smooth control signals. To produce a trajectory that satisfies all constraints a penalty method is applied to the nonlinear constraints. The efficacy of this proposed novel control scheme is successfully demonstrated through simulation results and comparisons, in terms of computation time and constraint violations, while are provided with respect to the number of agents.

【4】 Autonomous Curiosity for Real-Time Training Onboard Robotic Agents 标题：自主好奇心在机载机器人Agent实时训练中的应用 链接：https://arxiv.org/abs/2109.00927

作者：Ervin Teng,Bob Iannucci 备注：None 摘要：学习需要学习和好奇心。一个好的学习者不仅善于从提供给它的数据中提取信息，而且善于从中找到合适的新信息进行学习。当要求人工操作员提供基本事实时，尤其如此——这样的来源只应谨慎地进行查询。在这项工作中，我们解决了好奇心的问题，因为它涉及到在机器人平台上对目标检测算法进行在线、实时、人在回路训练，在机器人平台上，运动会产生新的主题视图。我们提出了一种深度强化学习方法，决定何时向人类用户询问地面真相，何时行动。通过一系列实验，我们证明我们的代理学习的移动和请求策略在使用人机交互来训练对象检测器方面比未经训练的方法至少有效3倍，并且可以推广到各种主题和环境。 摘要：Learning requires both study and curiosity. A good learner is not only good at extracting information from the data given to it, but also skilled at finding the right new information to learn from. This is especially true when a human operator is required to provide the ground truth - such a source should only be queried sparingly. In this work, we address the problem of curiosity as it relates to online, real-time, human-in-the-loop training of an object detection algorithm onboard a robotic platform, one where motion produces new views of the subject. We propose a deep reinforcement learning approach that decides when to ask the human user for ground truth, and when to move. Through a series of experiments, we demonstrate that our agent learns a movement and request policy that is at least 3x more effective at using human user interactions to train an object detector than untrained approaches, and is generalizable to a variety of subjects and environments.

【5】 KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator 标题：Kitti-Carla：由Carla模拟器生成的类似Kitti的数据集 链接：https://arxiv.org/abs/2109.00892

作者：Jean-Emmanuel Deschaud 机构： PSL University 摘要：KITTI-CARLA是从CARLA v0.9.10模拟器构建的数据集，使用与KITTI数据集相同的传感器车辆。因此，该车辆有一个位于屋顶中部的速度快堆HDL64激光雷达和类似于点灰色跳蚤2的两个彩色摄像机。激光雷达和摄像机的位置与在KITTI使用的设置相同。该数据集的目的是测试合成数据中的语义分割激光雷达和/或图像、里程计激光雷达和/或图像的方法，并与在真实数据（如KITTI）上获得的结果进行比较。因此，该数据集使得改进从合成数据集到真实数据集的转移学习方法成为可能。我们在卡拉的7张地图中创建了7个序列，每个序列中有5000帧，提供了不同的环境（城市、郊区、山区、农村、公路……）。该数据集可从以下网址获得：http://npm3d.fr 摘要：KITTI-CARLA is a dataset built from the CARLA v0.9.10 simulator using a vehicle with sensors identical to the KITTI dataset. The vehicle thus has a Velodyne HDL64 LiDAR positioned in the middle of the roof and two color cameras similar to Point Grey Flea 2. The positions of the LiDAR and cameras are the same as the setup used in KITTI. The objective of this dataset is to test approaches of semantic segmentation LiDAR and/or images, odometry LiDAR and/or image in synthetic data and to compare with the results obtained on real data like KITTI. This dataset thus makes it possible to improve transfer learning methods from a synthetic dataset to a real dataset. We created 7 sequences with 5000 frames in each sequence in the 7 maps of CARLA providing different environments (city, suburban area, mountain, rural area, highway...). The dataset is available at: http://npm3d.fr

【6】 Real-World Application of Various Trajectory Planning Algorithms on MIT RACECAR 标题：各种轨迹规划算法在麻省理工学院赛车上的实际应用 链接：https://arxiv.org/abs/2109.00890

作者：Oguzhan Kose 机构：ISTANBUL TECHNICAL UNIVERSITYFACULTY OF ELECTRICAL AND ELECTRONICS ENGINEERINGREAL-WORLD APPLICATION OF VARIOUSTRAJECTORY PLANNING ALGORITHMSON MIT RACECARSENIOR DESIGN PROJECTO˘guzhan KÖSEDepartment of Control and Automation EngineeringProject Advisor 摘要：在该项目中，车辆首先由ROS控制。为此，准备使用操纵杆控制必要的节点。然后，将DWA（动态窗口法）、TEB（定时弹性带）和APF（人工势场）路径规划算法分别应用于MIT赛车。这些算法在不同的问题上各有优缺点。因此，创建了一个场景来比较算法。在根据该场景创建的弯曲双车道道路上，麻省理工学院赛车必须沿着车道行驶，当遇到障碍物时，它必须在不离开道路的情况下改变车道，并在不撞到障碍物的情况下通过。此外，还开发了一种图像处理算法，以获取实现该场景所需的车道位置信息。该算法通过处理ZED摄像机拍摄的图像来检测目标点，并将目标点信息提供给路径规划算法。在创建了必要的工具之后，针对场景对算法进行了测试。在这些测试中，需要测量算法成功通过的障碍物数量、选择的简单路线以及计算成本。根据这些结果，虽然它不是成功通过大多数障碍的算法，但选择APF是因为它的低处理负载和简单的工作逻辑。据信，APF结构简单，在项目的未来阶段也将提供优势。 摘要：In the project, the vehicle was first controlled with ROS. For this purpose, the necessary nodes were prepared to be controlled with a joystick. Afterwards, DWA(Dynamic Window Approach), TEB(Timed-Elastic Band) and APF(Artificial Potential Field) path planning algorithms were applied to MIT RACECAR, respectively. These algorithms have advantages and disadvantages against each other on different issues. For this reason, a scenario was created to compare algorithms. On a curved double lane road created according to this scenario, MIT RACECAR has to follow the lanes and when it encounters an obstacle, it has to change lanes without leaving the road and pass without hitting the obstacle. In addition, an image processing algorithm was developed to obtain the position information of the lanes needed to implement this scenario. This algorithm detects the target point by processing the image taken from the ZED camera and gives the target point information to the path planning algorithm. After the necessary tools were created, the algorithms were tested against the scenario. In these tests, measurements such as how many obstacles the algorithm successfully passed, how simple routes it chose, and computational costs they have. According to these results, although it was not the algorithm that successfully passed the most obstacles, APF was chosen due to its low processing load and simple working logic. It was believed that with its uncomplicated structure, APF would also provide advantages in the future stages of the project.

【7】 MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization 标题：MACRPO：多Agent协同递归策略优化 链接：https://arxiv.org/abs/2109.00882

作者：Eshagh Kargar,Ville Kyrki 机构：Received: date Accepted: date 摘要：本文研究了在部分可观测的非平稳环境下，无通信信道的多智能体环境下的协作策略学习问题。我们致力于改进代理之间的信息共享，并提出了一种新的多代理参与者-批评家方法，称为\text{multi-agent-Cooperative-Policy-Optimization}（MACRPO）。我们提出了两种在MACRPO中跨代理和时间集成信息的新方法：首先，我们在critic的网络体系结构中使用递归层，并提出了一种新的框架，使用元轨迹来训练递归层。这使网络能够了解代理之间的协作和交互动态，并处理部分可观测性。第二，我们提出了一个新的优势函数，它包含了其他代理的奖励和价值函数。我们在三个具有连续和离散动作空间的挑战性多智能体环境、Deepdrive Zero、multi Walker和粒子环境中评估了我们的算法。我们将结果与几种消融和最先进的多智能体算法（如QMIX和MADDPG）以及单智能体方法（如IMPALA和APEX）进行了比较。结果表明，与其他算法相比，该算法具有更好的性能。该守则可在以下网址查阅：https://github.com/kargarisaac/macrpo. 摘要：This work considers the problem of learning cooperative policies in multi-agent settings with partially observable and non-stationary environments without a communication channel. We focus on improving information sharing between agents and propose a new multi-agent actor-critic method called \textit{Multi-Agent Cooperative Recurrent Proximal Policy Optimization} (MACRPO). We propose two novel ways of integrating information across agents and time in MACRPO: First, we use a recurrent layer in critic's network architecture and propose a new framework to use a meta-trajectory to train the recurrent layer. This allows the network to learn the cooperation and dynamics of interactions between agents, and also handle partial observability. Second, we propose a new advantage function that incorporates other agents' rewards and value functions. We evaluate our algorithm on three challenging multi-agent environments with continuous and discrete action spaces, Deepdrive-Zero, Multi-Walker, and Particle environment. We compare the results with several ablations and state-of-the-art multi-agent algorithms such as QMIX and MADDPG and also single-agent methods with shared parameters between agents such as IMPALA and APEX. The results show superior performance against other algorithms. The code is available online at https://github.com/kargarisaac/macrpo.

【8】 VORRT-COLREGs: A Hybrid Velocity Obstacles and RRT Based COLREGs-Compliant Path Planner for Autonomous Surface Vessels 标题：VORRT-COLREGS：基于混合速度障碍物和RRT的自主水面舰艇COLREGS兼容路径规划器 链接：https://arxiv.org/abs/2109.00862

作者：Rahul Dubey,Sushil J Louis 备注：Accepted in IEEE OCEANS 2021 摘要：本文介绍了VORRT-COLREGs，一种结合速度障碍（VO）和快速探索随机树（RRT）的混合技术，以生成自主水面舰艇（ASV）在遵守航海规则的同时的安全轨迹。RRT生成一组路径点，速度障碍物方法确保路径点之间的安全行驶。我们还确保ASV的行动不违反海上碰撞指南。早期的工作分别使用RRT和VO为ASV生成路径。然而，RRT不能很好地处理高度动态的情况，而VO似乎最适合作为本地路径规划器。结合这两种方法，VORRT COLREGs是一种全局路径规划器，它使用联合正向模拟来确保生成的路径在情况变化时保持有效和无碰撞。实验在不同类型的碰撞场景和不同数量的ASV中进行。结果表明，VORRT-COLREGS在公海场景中生成碰撞规则（COLREGS）投诉路径。此外，VORRT-COLREGS在交通分流方案中成功生成了兼容路径。这些结果显示了我们的技术在不同碰撞场景中为ASV生成路径的适用性。据我们所知，这是第一次将速度障碍物和RRT结合起来，为ASV提供安全且可靠的投诉路径。 摘要：This paper presents VORRT-COLREGs, a hybrid technique that combines velocity obstacles (VO) and rapidly-exploring random trees (RRT) to generate safe trajectories for autonomous surface vessels (ASVs) while following nautical rules of the road. RRT generates a set of way points and the velocity obstacles method ensures safe travel between way points. We also ensure that the actions of ASVs do not violate maritime collision guidelines. Earlier work has used RRT and VO separately to generate paths for ASVs. However, RRT does not handle highly dynamic situations well and and VO seems most suitable as a local path planner. Combining both approaches, VORRT-COLREGs is a global path planner that uses a joint forward simulation to ensure that generated paths remain valid and collision free as the situation changes. Experiments were conducted in different types of collision scenarios and with different numbers of ASVs. Results show that VORRT-COLREGS generated collision regulations (COLREGs) complaint paths in open ocean scenarios. Furthermore, VORRT-COLREGS successfully generated compliant paths within traffic separation schemes. These results show the applicability of our technique for generating paths for ASVs in different collision scenarios. To the best of our knowledge, this is the first work that combines velocity obstacles and RRT to produce safe and COLREGs complaint path for ASVs.

【9】 User, Robot, Deployer: A New Model for Measuring Trust in HRI 标题：用户、机器人、部署者：HRI信任度量的新模型 链接：https://arxiv.org/abs/2109.00861

作者：David Cameron,Emily C. Collins 机构：Information School, The University of Sheffield, Sheffield, UK., Department of Computer Science, University of Manchester, Manchester, UK., ORCID ,-,-,-,X 备注：In proceedings of SCRITA 2021 (this https URL), a workshop at IEEE RO-MAN 2021: this https URL 摘要：考虑、实现和测量人机交互（HRI）中的信任越来越受到人们的关注。通常，这集中于在HRI框架内影响用户信任，作为机器人和用户之间的二元交互。我们认为这忽略了一个关键的复杂性：机器人的可信度也可能取决于用户与部署机器人的个人或组织的关系和意见。我们新的HRI三元组模型（用户、机器人、部署者）为更全面地考虑和衡量信任提供了新的预测。 摘要：There is an increasing interest in considering, implementing, and measuring trust in human-robot interaction (HRI). Typically, this centres on influencing user trust within the framing of HRI as a dyadic interaction between robot and user. We propose this misses a key complexity: a robot's trustworthiness may also be contingent on the user's relationship with, and opinion of, the individual or organisation deploying the robot. Our new HRI triad model (User, Robot, Deployer), offers novel predictions for considering and measuring trust more completely.

【10】 Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse 标题：温室番茄单次激发多盒检测器和YOLO深度学习模型的评价 链接：https://arxiv.org/abs/2109.00810

作者：Sandro A. Magalhães,Luís Castro,Germano Moreira,Filipe N. Santos,mário Cunha,Jorge Dias,António P. Moreira 机构： Filipe Neves dos Santos ,, and António Paulo Moreira , ����������, Citation: Magalhães, S.; Castro, L.;, Moreira, G.; dos Santos, F.N.; Cunha, M.; Dias, J.; Moreira, A.P., EvaluatingtheSingle-Shot MultiBox, Detector and YOLO Deep Learning 备注：None 摘要：农业机器人解决方案的开发需要先进的感知能力，能够在任何作物阶段可靠地工作。例如，为了自动化温室中的番茄收获过程，视觉感知系统需要在任何生命周期阶段（从开花到成熟的番茄）检测番茄。视觉番茄检测的最新技术主要集中在成熟番茄上，它与背景颜色不同。本文提供了一个绿色和红色西红柿的注释可视化数据集。这种数据集不常见，不可用于研究目的。这将使edge人工智能的进一步发展成为可能，用于开发收获机器人所需的原位和实时可视化番茄检测。考虑到这个数据集，选择了五个深度学习模型，对其进行训练和基准测试，以检测温室中生长的绿色和红色西红柿。考虑到我们的机器人平台规格，仅考虑了单点多盒探测器（SSD）和YOLO架构。结果证明，该系统可以检测绿色和红色的西红柿，甚至那些被叶子遮挡的西红柿。SSD MaMeNETET V2与SSD起始V2、SSD ResNET 50、SSD ResNET 101和YOLVO4微小相比，达到最佳的性能，达到NFIDIA TRAIN架构平台51.46%的51.46%和16.44 ms的推断时间，英伟达TESLA T4，12 GB。YOLOv4 Tiny也有令人印象深刻的结果，主要是关于大约5毫秒的推断时间。 摘要：The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15%, an mAP of 51.46% and an inference time of 16.44 ms with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5 ms.

【11】 MIR-VIO: Mutual Information Residual-based Visual Inertial Odometry with UWB Fusion for Robust Localization 标题：MIR-VIO：基于互信息残差的视觉惯性里程计与UWB融合的鲁棒定位 链接：https://arxiv.org/abs/2109.00747

作者：Sungjae Shin,Eungchang Lee,Junho Choi,Hyun Myung 机构：School of Electrical Engineering, KAIST, School of Electrical Engineering, KI-AI, KI-R, KAIST 摘要：多年来，视觉里程计在移动机器人和无人机上的应用取得了令人瞩目的进展。然而，视觉感知仍然是一个具有挑战性的领域，因为视觉传感器在使用单目相机获取正确的尺度信息方面存在一些问题，并且容易受到照明变化情况的影响。在本文中，超宽带传感器融合被提出在视觉惯性里程计算法作为一种解决方案，以缓解这一问题。考虑到UWB，我们设计了一个基于互信息的代价函数。考虑到UWB信号模型的特点，即不确定性随着UWB锚和标签之间的距离增加而增加，我们在代价函数中引入了一个新的残差项。采用上述方法在室内环境下进行实验，通过超宽带传感器融合解决了特征点较少的环境下的初始化问题，使定位变得鲁棒。当使用互信息概念的残差项时，可以得到最稳健的里程计。 摘要：For many years, there has been an impressive progress on visual odometry applied to mobile robots and drones. However, the visual perception is still in the spotlight as a challenging field because the vision sensor has some problems in obtaining correct scale information with a monocular camera and also is vulnerable to a situation in which illumination is changed. In this paper, UWB sensor fusion is proposed in the visual inertial odometry algorithm as a solution to mitigate this problem. We designed a cost function based on mutual information considering the UWB. Considering the characteristic of the UWB signal model, where the uncertainty increases as the distance between the UWB anchor and the tag increases, we introduced a new residual term to the cost function. When the experiment was conducted in an indoor environment with the above methodology, the initialization problem in an environment with few feature points was solved through the UWB sensor fusion, and localization became robust. And when the residual term using the concept of mutual information was used, the most robust odometry could be obtained.

【12】 Time-correlated Window Carrier-phase Aided GNSS Positioning Using Factor Graph Optimization for Urban Positioning 标题：基于因子图优化的时间相关窗载波相位辅助GNSS城市定位 链接：https://arxiv.org/abs/2109.00683

作者：Xiwei Bai,Weisong Wen,Li-Ta Hsu 机构：proposed by introducing additional information or sensors, such as ,D mapping aided GNSS [,-,], camera aided GNSS, NLOS detection [,-,], and ,D LiDAR aided GNSS NLOS, detection [,] or correction [,]. Unfortunately, these 摘要：本文提出了一种改进的全球导航卫星系统（GNSS）定位方法，该方法探索了连续码元和载波相位测量之间的时间相关性，从而显著提高了对异常测量的鲁棒性。本文提出了利用窗载波相位（WCP）内的载波相位测量值来约束因子图内的状态，而不是依赖于使用扩展卡尔曼滤波器（EKF）估计器仅考虑两个相邻时段的时差载波相位（TDCP）。左零空间矩阵用于消除共享的未知模糊变量，从而关联WCP内的相关状态。然后，使用因子图优化（FGO）同时集成伪距、多普勒和构造的WCP测量值，以估计GNSS接收机的状态。我们在香港的两个典型的城市峡谷中评估了所提出的方法的性能，使用汽车级GNSS接收器分别获得了1.76米和2.96米的平均定位误差。同时，通过一个低成本的智能手机级GNSS接收机对该方法的有效性进行了进一步评估，并与现有的几种GNSS定位方法进行了比较，得到了类似的改进。 摘要：This paper proposes an improved global navigation satellite system (GNSS) positioning method that explores the time correlation between consecutive epochs of the code and carrier phase measurements which significantly increases the robustness against outlier measurements. Instead of relying on the time difference carrier phase (TDCP) which only considers two neighboring epochs using an extended Kalman filter (EKF) estimator, this paper proposed to employ the carrier-phase measurements inside a window, the so-called window carrier-phase (WCP), to constrain the states inside a factor graph. A left null space matrix is employed to eliminate the shared unknown ambiguity variables and therefore, correlated the associated states inside the WCP. Then the pseudorange, Doppler, and the constructed WCP measurements are integrated simultaneously using factor graph optimization (FGO) to estimate the state of the GNSS receiver. We evaluated the performance of the proposed method in two typical urban canyons in Hong Kong, achieving the mean positioning error of 1.76 meters and 2.96 meters, respectively, using the automobile-level GNSS receiver. Meanwhile, the effectiveness of the proposed method is further evaluated using a low-cost smartphone level GNSS receiver and similar improvement is also obtained, compared with several existing GNSS positioning methods.

【13】 GNSS Outlier Mitigation Via Graduated Non-Convexity Factor Graph Optimization 标题：基于梯度非凸因子图优化的GNSS野值剔除 链接：https://arxiv.org/abs/2109.00667

作者：Weisong Wen,Guohao Zhang,Li-Ta Hsu 机构：[,]; (,) GNSS outlier mitigation based on robust models, by making use of the historical batch data, such as the, switchable constraints [,], and dynamic covariance estimation, [,]. The following sub-section presents the existing works that 摘要：基于全球导航卫星系统（GNSS）的精确和全球参考车辆定位可以在无离群点的开放区域实现。然而，由于建筑物的信号反射引起的多径效应和非视距（NLOS）接收等异常测量，全球导航卫星系统的性能会显著降低。受批量历史数据在抵抗异常值测量方面的优势启发，本文提出了一种分级非凸性因子图优化（FGO-GNC）来提高GNSS定位性能，通过估计GNSS测量值的最佳权重来减轻GNSS异常值的影响。与现有的局部解不同，所提出的FGO-GNC采用非凸Geman-McClure（GM）函数通过从粗到细的松弛来全局估计GNSS测量值的权重。所提出的方法的有效性是通过几个挑战性的数据收集在香港城市峡谷使用汽车级和低成本的智能手机级GNSS接收器进行验证。 摘要：Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers.

【14】 Quori: A Community-Informed Design of a Socially Interactive Humanoid Robot 标题：Quori：一种社群知情的社交型人形机器人设计 链接：https://arxiv.org/abs/2109.00662

作者：Andrew Specian,Ross Mead,Simon Kim,Maja Matarić,Mark Yim 备注：20 pages. 21 figures. This was accepted to and will be published to the IEEE Transactions on Robotics Journal 摘要：社交互动机器人的硬件平台可能会受到成本或功能不足的限制。本文介绍了Quori的总体系统设计、硬件和软件。Quori是一种新颖、经济、社会互动的仿人机器人平台，用于促进非接触式人机交互（HRI）研究。该系统的设计受到来自HRI研究社区的反馈的激励。总体设计保持了经济性和功能性的平衡。介绍了初始Quori测试和六个月的部署。十个Quori平台已授予来自美国各地的不同研究人员群体，以促进HRI研究从一个通用平台构建社区数据库。 摘要：Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This paper presents the overall system -- design, hardware, and software -- for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating non-contact human-robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform.

机器翻译，仅供参考