机器人相关学术速递[7.19]

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cs.RO机器人相关，共计13篇

【1】 Is attention to bounding boxes all you need for pedestrian action prediction? 标题：要预测行人的行动，只需要注意边界框就行了吗？

作者：Lina Achaji,Julien Moreau,Thibault Fouqueray,Francois Aioun,Francois Charpillet 机构：Stellantis Group, Technical center of Velizy , France, Inria, Nancy , France 链接：https://arxiv.org/abs/2107.08031 摘要：人类驾驶员不再是唯一关心驾驶场景复杂性的人。自动驾驶汽车（AV）也同样参与了这一过程。如今，城市地区视听技术的发展为行人等易受伤害的道路使用者（VRU）提供了重要的安全保障。因此，为了使道路更安全，对其未来行为进行分类和预测是至关重要的。本文提出了一个基于多重变型变换模型的行人过街行为分析框架，对行人过街和不过街行为进行了分析和预测。我们证明了仅使用边界框作为模型的输入可以比以前的最新模型有更好的性能，并且在PIE数据集上达到91%的预测准确率和0.83的F1分数，在未来最多提前2秒。此外，我们还介绍了一个使用CARLA进行动作预测的大型模拟数据集（CP2A）。我们的模型在这个数据集上同样达到了高精度（91%）和F1分数（0.91）。有趣的是，我们证明了在模拟数据集上预先训练Transformer模型，然后在真实数据集上对其进行微调，对于动作预测任务是非常有效的。 摘要：The human driver is no longer the only one concerned with the complexity of the driving scenarios. Autonomous vehicles (AV) are similarly becoming involved in the process. Nowadays, the development of AV in urban places underpins essential safety concerns for vulnerable road users (VRUs) such as pedestrians. Therefore, to make the roads safer, it is critical to classify and predict their future behavior. In this paper, we present a framework based on multiple variations of the Transformer models to reason attentively about the dynamic evolution of the pedestrians' past trajectory and predict its future actions of crossing or not crossing the street. We proved that using only bounding boxes as input to our model can outperform the previous state-of-the-art models and reach a prediction accuracy of 91 % and an F1-score of 0.83 on the PIE dataset up to two seconds ahead in the future. In addition, we introduced a large-size simulated dataset (CP2A) using CARLA for action prediction. Our model has similarly reached high accuracy (91 %) and F1-score (0.91) on this dataset. Interestingly, we showed that pre-training our Transformer model on the simulated dataset and then fine-tuning it on the real dataset can be very effective for the action prediction task.

【2】 Learning Locomotion Controllers for Walking Using Deep FBSDE 标题：基于深度FBSDE的步行运动控制器学习

作者：Bolun Dai,Virinchi Roy Surabhi,Prashanth Krishnamurthy,Farshad Khorrami 机构：TandonSchoolofEngineering(PolytechnicInstitute), NewYorkUniversity 备注：Submitted to IROS 链接：https://arxiv.org/abs/2107.07931 摘要：本文提出了一种基于深度正倒向随机微分方程（FBSDE）的运动控制算法。我们还包括在FBSDE配方中的状态约束，以施加稳定的步行解决方案或其他可能需要考虑的约束（例如，能量）。我们的方法利用深层神经网络（即LSTM）来求解通常由所述最优控制问题产生的高维Hamilton-Jacobi-Bellman（HJB）方程。与传统方法相比，该方法具有更高的实时计算效率；从而产生闭环控制器的更高频率实现。该方法的有效性在一个线性倒立摆模型（LIPM）上得到了验证。即使我们正在部署一个简化的步行模型，该方法也适用于机器人系统中步行和其他控制/优化任务的广义和复杂模型。仿真研究表明了该方法的有效性。 摘要：In this paper, we propose a deep forward-backward stochastic differential equation (FBSDE) based control algorithm for locomotion tasks. We also include state constraints in the FBSDE formulation to impose stable walking solutions or other constraints that one may want to consider (e.g., energy). Our approach utilizes a deep neural network (i.e., LSTM) to solve, in general, high-dimensional Hamilton-Jacobi-Bellman (HJB) equation resulting from the stated optimal control problem. As compared to traditional methods, our proposed method provides a higher computational efficiency in real-time; thus yielding higher frequency implementation of the closed-loop controllers. The efficacy of our approach is shown on a linear inverted pendulum model (LIPM) for walking. Even though we are deploying a simplified model of walking, the methodology is applicable to generalized and complex models for walking and other control/optimization tasks in robotic systems. Simulation studies have been provided to show the effectiveness of the proposed methodology.

【3】 Safety in human-multi robot collaborative scenarios: a trajectory scaling approach 标题：人-多机器人协作场景中的安全性：一种轨迹缩放方法

作者：Martina Lippi,Alessandro Marino 机构：University of Salerno, Via Giovanni Paolo II, Salerno 备注：None 链接：https://arxiv.org/abs/2107.07921 摘要：本文设计了一种多机器人场景下的人身安全策略。在该框架中，可以预见机器人负责执行任何由适当的任务函数参数化的协同操作任务。这种结构满足了日益增长的人与机器人之间严格合作的需求，因为它配备了一个通用的多机器人单元，具有使机器人与人协同工作的特点。通过定义一个同时依赖于操作者和机器人的相对位置和相对速度的安全指标来正确处理人的安全问题。然后，对多机器人的任务轨迹进行适当的缩放，以保证人身安全不低于给定的阈值，该阈值可以根据最小允许距离在最坏的情况下设定。仿真结果验证了该方法的有效性。 摘要：In this paper, a strategy to handle the human safety in a multi-robot scenario is devised. In the presented framework, it is foreseen that robots are in charge of performing any cooperative manipulation task which is parameterized by a proper task function. The devised architecture answers to the increasing demand of strict cooperation between humans and robots, since it equips a general multi-robot cell with the feature of making robots and human working together. The human safety is properly handled by defining a safety index which depends both on the relative position and velocity of the human operator and robots. Then, the multi-robot task trajectory is properly scaled in order to ensure that the human safety never falls below a given threshold which can be set in worst conditions according to a minimum allowed distance. Simulations results are presented in order to prove the effectiveness of the approach.

【4】 Weakly-Supervised Object Detection Learning through Human-Robot Interaction 标题：人机交互的弱监督目标检测学习

作者：Elisa Maiettini,Vadim Tikhanoff,Lorenzo Natale 备注：None 链接：https://arxiv.org/abs/2107.07901 摘要：可靠的感知和对新环境的有效适应是类人机器人在动态环境中工作的首要技能。深度学习方法带来的最新计算机视觉研究的巨大进步吸引着机器人界。然而，它们在应用领域中的应用并不简单，因为使它们适应新的任务对注释数据和优化时间有很高的要求。然而，机器人平台，尤其是人形机器人，提供了一些机会（例如额外的传感器和探索环境的机会），可以利用这些机会来克服这些问题。在本文中，我们提出了一个管道，有效地训练一个目标检测系统的仿人机器人。该系统通过利用：（i）教师-学习者管道，（ii）弱监督学习技术以减少人类标记工作和（iii）用于快速模型再训练的在线学习方法，允许迭代地使目标检测模型适应新的场景。我们使用R1仿人机器人对所提出的管道进行实时测试，并采集图像序列对该方法进行测试。我们公开了应用程序的代码。 摘要：Reliable perception and efficient adaptation to novel conditions are priority skills for humanoids that function in dynamic environments. The vast advancements in latest computer vision research, brought by deep learning methods, are appealing for the robotics community. However, their adoption in applied domains is not straightforward since adapting them to new tasks is strongly demanding in terms of annotated data and optimization time. Nevertheless, robotic platforms, and especially humanoids, present opportunities (such as additional sensors and the chance to explore the environment) that can be exploited to overcome these issues. In this paper, we present a pipeline for efficiently training an object detection system on a humanoid robot. The proposed system allows to iteratively adapt an object detection model to novel scenarios, by exploiting: (i) a teacher-learner pipeline, (ii) weakly supervised learning techniques to reduce the human labeling effort and (iii) an on-line learning approach for fast model re-training. We use the R1 humanoid robot for both testing the proposed pipeline in a real-time application and acquire sequences of images to benchmark the method. We made the code of the application publicly available.

【5】 Versatile modular neural locomotion control with fast learning 标题：具有快速学习功能的多功能模块化神经运动控制

作者：Mathias Thor,Poramate Manoonpong 机构：Embodied AI and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, The University of Southern Denmark, Campusvej , Odense , Denmark, Bio-Inspired Robotics and Neural Engineering Laboratory 备注：For supplementary video files see: this https URL 链接：https://arxiv.org/abs/2107.07844 摘要：腿部机器人在高度非结构化的环境中有着巨大的潜力。然而，运动控制的设计仍然具有挑战性。目前，控制器必须为特定的机器人和任务手动设计，或者通过机器学习方法自动设计，这些方法需要较长的训练时间并产生大量不透明的控制器。从动物运动的启发，我们提出了一个简单而通用的模块化快速学习神经控制结构。该方法的主要优点是可以逐步增加特定于行为的控制模块，以获得日益复杂的紧急运动行为，并且可以快速自动地学习与现有模块接口的神经连接。在一系列实验中，我们展示了如何快速学习八个模块，并将其添加到一个基本控制模块中，以获得紧急的自适应行为，从而使六足机器人能够在复杂环境中导航。我们还表明，模块可以在操作过程中添加和删除，而不影响其余控制器的功能。最后，在一个物理六足机器人上成功地演示了该控制方法。综上所述，我们的研究为复杂机器人系统的多功能神经运动控制的快速自动设计迈出了重要的一步。 摘要：Legged robots have significant potential to operate in highly unstructured environments. The design of locomotion control is, however, still challenging. Currently, controllers must be either manually designed for specific robots and tasks, or automatically designed via machine learning methods that require long training times and yield large opaque controllers. Drawing inspiration from animal locomotion, we propose a simple yet versatile modular neural control structure with fast learning. The key advantages of our approach are that behavior-specific control modules can be added incrementally to obtain increasingly complex emergent locomotion behaviors, and that neural connections interfacing with existing modules can be quickly and automatically learned. In a series of experiments, we show how eight modules can be quickly learned and added to a base control module to obtain emergent adaptive behaviors allowing a hexapod robot to navigate in complex environments. We also show that modules can be added and removed during operation without affecting the functionality of the remaining controller. Finally, the control approach was successfully demonstrated on a physical hexapod robot. Taken together, our study reveals a significant step towards fast automatic design of versatile neural locomotion control for complex robotic systems.

【6】 Attention-based Vehicle Self-Localization with HD Feature Maps 标题：基于注意力的高清特征地图车辆自定位

作者：Nico Engel,Vasileios Belagiannis,Klaus Dietmayer 机构：© , IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprintingrepublishing 备注：Accepted for publication at 24th IEEE International Conference on Intelligent Transportation Systems (ITSC 2021) 链接：https://arxiv.org/abs/2107.07787 摘要：提出了一种基于点的深度神经网络的车辆自定位方法。我们的方法处理测量和点特征，即地标，从高清数字地图推断车辆的姿态。为了学习最佳关联，并结合点集之间的局部信息，我们提出了一种将测量值与相应的地标相匹配的注意机制。最后，我们将此表示法应用于点云配准和后续的姿态回归任务。此外，我们还引入了一个训练模拟框架，该框架可以人为地生成度量和地标，以方便部署过程，并降低从真实数据创建大量数据集的成本。我们在我们的数据集上评估了我们的方法，以及Kitti里程计数据集的一个改编版本，与相关方法相比，我们取得了更高的性能；此外，还表现出明显的泛化能力。 摘要：We present a vehicle self-localization method using point-based deep neural networks. Our approach processes measurements and point features, i.e. landmarks, from a high-definition digital map to infer the vehicle's pose. To learn the best association and incorporate local information between the point sets, we propose an attention mechanism that matches the measurements to the corresponding landmarks. Finally, we use this representation for the point-cloud registration and the subsequent pose regression task. Furthermore, we introduce a training simulation framework that artificially generates measurements and landmarks to facilitate the deployment process and reduce the cost of creating extensive datasets from real-world data. We evaluate our method on our dataset, as well as an adapted version of the Kitti odometry dataset, where we achieve superior performance compared to related approaches; and additionally show dominant generalization capabilities.

【7】 LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping 标题：LT-mapper：一种基于LiDAR的终身地图模块化框架

作者：Giseop Kim,Ayoung Kim 机构： Kim is with the Department of Civil and Environmental Engineering, Kim is with Department of Civil and Environmental Engineering 链接：https://arxiv.org/abs/2107.07712 摘要：长期三维地图管理是机器人在非静止的现实世界中可靠导航所需的基本能力。本文开发了一个开源的、模块化的、易于使用的基于LiDAR的城市站点终身地图。这是通过将问题划分为连续的子问题来实现的：多会话SLAM（MSS）、高/低动态变化检测和正/负变化管理。该方法充分利用了MSS技术，处理了潜在的弹道误差；因此，变更检测不需要良好的初始对准。我们的变更管理方案在内存和计算成本方面都保持了有效性，提供了从大规模点云地图中自动分离对象的功能。我们通过对多个时间间隔（从一天到一年）的大量实际实验，验证了该框架的可靠性和适用性。 摘要：Long-term 3D map management is a fundamental capability required by a robot to reliably navigate in the non-stationary real-world. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. This is achieved by dividing the problem into successive subproblems: multi-session SLAM (MSS), high/low dynamic change detection, and positive/negative change management. The proposed method leverages MSS and handles potential trajectory error; thus, good initial alignment is not required for change detection. Our change management scheme preserves efficacy in both memory and computation costs, providing automatic object segregation from a large-scale point cloud map. We verify the framework's reliability and applicability even under permanent year-level variation, through extensive real-world experiments with multiple temporal gaps (from day to year).

【8】 Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness 标题：基于新位置感知的概率外观不变拓扑定位

作者：Ming Xu,Tobias Fischer,Niko Sünderhauf,Michael Milford 机构： All authors acknowledgecontinued support from the Queensland University of Technology (QUT)through the Centre for Robotics, QueenslandUniversityofTechnology 备注：None 链接：https://arxiv.org/abs/2107.07707 摘要：概率状态估计方法为设计定位系统提供了一个有原则的基础，因为它们自然地整合了不完美运动序列和外感传感器数据序列。近年来，利用外观不变视觉位置识别（VPR）方法作为主要外部感知传感器的概率定位系统在外观发生显著变化的情况下表现出了最先进的性能。然而，现有系统1）没有充分利用运动模型中的里程数据，2）无法处理路线偏差，因为假设查询遍历正好重复映射遍历。为了克服这些缺点，我们提出了一种新的概率拓扑定位系统，该系统将全三自由度里程计纳入运动模型，并在状态估计框架中加入了“地图外”状态，允许从参考地图成功定位具有重要路线迂回的查询遍历。我们对来自Oxford RobotCar数据集的多个查询遍历进行了广泛的评估，显示出显著的外观变化和与先前遍历的路由的偏差。特别地，我们评估了两个实际相关的定位任务的性能：循环闭合检测和全局定位。与现有的和改进的最先进的系统相比，我们的方法实现了重大的性能改进。 摘要：Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data. Recently, probabilistic localization systems utilizing appearance-invariant visual place recognition (VPR) methods as the primary exteroceptive sensor have demonstrated state-of-the-art performance in the presence of substantial appearance change. However, existing systems 1) do not fully utilize odometry data within the motion models, and 2) are unable to handle route deviations, due to the assumption that query traverses exactly repeat the mapping traverse. To address these shortcomings, we present a new probabilistic topometric localization system which incorporates full 3-dof odometry into the motion model and furthermore, adds an "off-map" state within the state-estimation framework, allowing query traverses which feature significant route detours from the reference map to be successfully localized. We perform extensive evaluation on multiple query traverses from the Oxford RobotCar dataset exhibiting both significant appearance change and deviations from routes previously traversed. In particular, we evaluate performance on two practically relevant localization tasks: loop closure detection and global localization. Our approach achieves major performance improvements over both existing and improved state-of-the-art systems.

【9】 Constrained Feedforward Neural Network Training via Reachability Analysis 标题：基于可达性分析的约束前馈神经网络训练

作者：Long Kiu Chung,Adam Dai,Derek Knowles,Shreyas Kousik,Grace X. Gao 机构： Dai is with the Department of Electrical Engineering 备注：5 pages, 4 figures 链接：https://arxiv.org/abs/2107.07696 摘要：最近，神经网络的应用越来越广泛，但在诸如人类附近和周围的机器人等安全关键领域的应用有限。这是因为训练神经网络服从安全约束仍然是一个公开的挑战。大多数现有的安全相关方法只寻求验证已经训练过的网络是否服从约束，需要交替训练和验证。相反，本文提出了一种同时训练和验证具有校正线性单元（ReLU）非线性的前馈神经网络的约束方法。通过计算网络的输出空间可达集并确保其不与不安全集相交来实施约束；训练是通过在可达集和输出空间的不安全部分之间建立一个新的冲突检查损失函数来实现的。可达集和不安全集由约束的zonotopes表示，这是一种凸多面体表示，支持可微碰撞检查。该方法在一个具有一个非线性层和大约50个参数的网络上得到了成功的验证。 摘要：Neural networks have recently become popular for a wide variety of uses, but have seen limited application in safety-critical domains such as robotics near and around humans. This is because it remains an open challenge to train a neural network to obey safety constraints. Most existing safety-related methods only seek to verify that already-trained networks obey constraints, requiring alternating training and verification. Instead, this work proposes a constrained method to simultaneously train and verify a feedforward neural network with rectified linear unit (ReLU) nonlinearities. Constraints are enforced by computing the network's output-space reachable set and ensuring that it does not intersect with unsafe sets; training is achieved by formulating a novel collision-check loss function between the reachable set and unsafe portions of the output space. The reachable and unsafe sets are represented by constrained zonotopes, a convex polytope representation that enables differentiable collision checking. The proposed method is demonstrated successfully on a network with one nonlinearity layer and approximately 50 parameters.

【10】 CMU-GPR Dataset: Ground Penetrating Radar Dataset for Robot Localization and Mapping 标题：CMU-GPR数据集：用于机器人定位和测绘的探地雷达数据集

作者：Alexander Baikovitz,Paloma Sodhi,Michael Dille,Michael Kaess 机构： 1 The Robotics Institute, Carnegie Mellon University 备注：Accepted to the Radar in Robotics Workshop at the 2021 International Conference on Robotics and Automation 链接：https://arxiv.org/abs/2107.07606 摘要：雷达作为机器人导航的传感器，由于其对各种环境条件的鲁棒性增强，近年来取得了令人振奋的进展。然而，在这些不同的雷达感知系统中，探地雷达（GPR）仍处于探索阶段。通过测量地下结构，探地雷达可以提供稳定的特征，这些特征对环境天气、场景和光照变化的变化较小，因此成为长期时空制图的一个令人信服的选择。在这项工作中，我们提出了CMU-GPR数据集——一个开源的探地雷达数据集，用于机器人导航的地下辅助感知研究。总的来说，数据集包含15个不同的轨迹序列在3个GPS拒绝，室内环境。从探地雷达、车轮编码器、RGB相机和惯性测量单元收集测量数据，并从机器人全站仪获取地面真实位置。除了数据集，我们还提供了实用程序代码，将原始探地雷达数据转换为处理后的图像。本文描述了我们的记录平台、数据格式、实用脚本以及使用这些数据的方法。 摘要：There has been exciting recent progress in using radar as a sensor for robot navigation due to its increased robustness to varying environmental conditions. However, within these different radar perception systems, ground penetrating radar (GPR) remains under-explored. By measuring structures beneath the ground, GPR can provide stable features that are less variant to ambient weather, scene, and lighting changes, making it a compelling choice for long-term spatio-temporal mapping. In this work, we present the CMU-GPR dataset--an open-source ground penetrating radar dataset for research in subsurface-aided perception for robot navigation. In total, the dataset contains 15 distinct trajectory sequences in 3 GPS-denied, indoor environments. Measurements from a GPR, wheel encoder, RGB camera, and inertial measurement unit were collected with ground truth positions from a robotic total station. In addition to the dataset, we also provide utility code to convert raw GPR data into processed images. This paper describes our recording platform, the data format, utility scripts, and proposed methods for using this data.

【11】 Partially Observable Markov Decision Processes (POMDPs) and Robotics 标题：部分可观测马尔可夫决策过程(POMDP)与机器人

作者：Hanna Kurniawati 机构：School of Computing, Australian National University 链接：https://arxiv.org/abs/2107.07599 摘要：不确定环境下的规划对机器人技术至关重要。部分可观测马尔可夫决策过程（POMDP）是解决此类规划问题的数学框架。它是强大的，因为它仔细量化的非确定性影响的行动和部分可观测的状态。但正因为如此，POMDP以其高计算复杂度而臭名昭著，并被认为不适用于机器人。然而，自2000年初以来，由于基于采样的近似解算器，POMDPs的求解能力有了很大的提高。虽然这些解算器不产生最优解，但它们可以在合理的计算资源范围内计算出良好的POMDP解，显著提高机器人系统的鲁棒性，从而使POMDP在许多现实机器人问题中具有实用性。本文对POMDPs进行了综述，重点介绍了阻碍其在机器人学中应用的计算问题和基于采样的求解器的思想，以及将POMDPs应用于物理机器人的经验教训。 摘要：Planning under uncertainty is critical to robotics. The Partially Observable Markov Decision Process (POMDP) is a mathematical framework for such planning problems. It is powerful due to its careful quantification of the non-deterministic effects of actions and partial observability of the states. But precisely because of this, POMDP is notorious for its high computational complexity and deemed impractical for robotics. However, since early 2000, POMDPs solving capabilities have advanced tremendously, thanks to sampling-based approximate solvers. Although these solvers do not generate the optimal solution, they can compute good POMDP solutions that significantly improve the robustness of robotics systems within reasonable computational resources, thereby making POMDPs practical for many realistic robotics problems. This paper presents a review of POMDPs, emphasizing computational issues that have hindered its practicality in robotics and ideas in sampling-based solvers that have alleviated such difficulties, together with lessons learned from applying POMDPs to physical robots.

【12】 A Comparison of Modern General-Purpose Visual SLAM Approaches 标题：现代通用视觉SLAM方法的比较

作者：Alexey Merzlyakov,Steve Macenski 机构：Samsung Research Russia, Samsung Research America 备注：IROS 2021 链接：https://arxiv.org/abs/2107.07589 摘要：移动和腿机器人技术的成熟正在改变机器人部署和发现的格局。这一创新要求同时定位和映射（SLAM）系统的转型，以支持新一代服务和消费机器人。传统上坚固的二维激光雷达系统不再占主导地位，而机器人正部署在多层室内、室外非结构化和城市领域，使用越来越便宜的立体声和RGB-D相机。可视化SLAM（visualslam）系统是研究了几十年的一个主题，有少数公开的实现脱颖而出：ORB-SLAM3、OpenVSLAM和RTABMap。本文使用服务机器人协商的跨多个域的多个不同数据集，对这3种现代的、功能丰富的、唯一健壮的VSLAM技术进行了比较。ORB-SLAM3和OpenVSLAM都没有与之前文献中的至少一个数据集进行比较，我们通过这个镜头提供了见解。本分析旨在寻找通用、功能完整和多域的VSLAM选项，以支持广泛的机器人应用，并将其集成到新的和改进的ROS 2 Nav2系统中，作为传统2D激光雷达解决方案的合适替代方案。 摘要：Advancing maturity in mobile and legged robotics technologies is changing the landscapes where robots are being deployed and found. This innovation calls for a transformation in simultaneous localization and mapping (SLAM) systems to support this new generation of service and consumer robots. No longer can traditionally robust 2D lidar systems dominate while robots are being deployed in multi-story indoor, outdoor unstructured, and urban domains with increasingly inexpensive stereo and RGB-D cameras. Visual SLAM (VSLAM) systems have been a topic of study for decades and a small number of openly available implementations have stood out: ORB-SLAM3, OpenVSLAM and RTABMap. This paper presents a comparison of these 3 modern, feature rich, and uniquely robust VSLAM techniques that have yet to be benchmarked against each other, using several different datasets spanning multiple domains negotiated by service robots. ORB-SLAM3 and OpenVSLAM each were not compared against at least one of these datasets previously in literature and we provide insight through this lens. This analysis is motivated to find general purpose, feature complete, and multi-domain VSLAM options to support a broad class of robot applications for integration into the new and improved ROS 2 Nav2 System as suitable alternatives to traditional 2D lidar solutions.

【13】 OdoViz: A 3D Odometry Visualization and Processing Tool 标题：OdoViz：一个三维里程计可视化与处理工具

作者：Saravanabalagi Ramachandran,John McDonald 机构：MaynoothUniversity 备注：Accepted, ITSC 2021 链接：https://arxiv.org/abs/2107.07557 摘要：OdoViz是一个基于web的反应式工具，用于自动车辆数据集的三维可视化和处理，旨在支持视觉位置识别研究中的常见任务。该系统包括加载、检查、可视化和处理GPS/INS姿态、点云和相机图像的功能。它支持许多常用的驱动数据集，并且可以调整为以最小的工作量加载自定义数据集。OdoViz的设计包括一个超薄的服务器和一个富客户端前端。这种设计支持多种部署配置，包括单用户独立安装、研究组安装（在实验室内部为数据集提供服务）或公开访问的web前端（用于提供用于探索和与数据集交互的在线接口）。该工具允许同时查看在多个不同时间段穿过的完整车辆轨迹，便于执行子采样、比较和查找序列间和序列内的姿势对应等任务。这大大减少了为机器学习任务从现有数据集创建数据子集所需的工作量。除此之外，该系统还支持添加自定义扩展和插件，以扩展软件用于其他潜在数据管理、可视化和处理任务的功能。该平台已经开源，以促进其使用，并鼓励研究界作出进一步贡献。 摘要：OdoViz is a reactive web-based tool for 3D visualization and processing of autonomous vehicle datasets designed to support common tasks in visual place recognition research. The system includes functionality for loading, inspecting, visualizing, and processing GPS/INS poses, point clouds and camera images. It supports a number of commonly used driving datasets and can be adapted to load custom datasets with minimal effort. OdoViz's design consists of a slim server to serve the datasets coupled with a rich client frontend. This design supports multiple deployment configurations including single user stand-alone installations, research group installations serving datasets internally across a lab, or publicly accessible web-frontends for providing online interfaces for exploring and interacting with datasets. The tool allows viewing complete vehicle trajectories traversed at multiple different time periods simultaneously, facilitating tasks such as sub-sampling, comparing and finding pose correspondences both across and within sequences. This significantly reduces the effort required in creating subsets of data from existing datasets for machine learning tasks. Further to the above, the system also supports adding custom extensions and plugins to extend the capabilities of the software for other potential data management, visualization and processing tasks. The platform has been open-sourced to promote its use and encourage further contributions from the research community.