Robotics has been widely applied in smart construction for generating the digital twin or for autonomous inspection of construction sites. For example, for thermal inspection during concrete curing, continual monitoring of the concrete temperature is required to ensure concrete strength and to avoid cracks. However, buildings are typically too large to be monitored by installing fixed thermal cameras, and post-processing is required to compute the accumulated heat of each measurement point. Thus, by using an autonomous monitoring system with the capability of long-term thermal mapping at a large construction site, both cost-effectiveness and a precise safety margin of the curing period estimation can be acquired. Therefore, this study proposes a low-cost thermal mapping system consisting of a 2D range scanner attached to a consumer-level inertial measurement unit and a thermal camera for automated heat monitoring in construction using mobile robots.

视觉惯性探测器和猛击算法广泛用于各种领域，例如服务机器人，无人机和自动驾驶汽车。大多数SLAM算法都是基于地标是静态的。但是，在现实世界中，存在各种动态对象，它们会降低姿势估计精度。此外，暂时的静态对象，在观察过程中是静态的，但在视线视线时移动，触发假循环封闭。为了克服这些问题，我们提出了一个新颖的视觉惯性大满贯框架，称为dynavins，它对动态对象和暂时静态对象都具有强大的态度。在我们的框架中，我们首先提出一个可靠的捆绑捆绑调整，该调整可以通过利用IMU预融合估计的姿势先验来拒绝动态对象的功能。然后，提出了一个密钥帧分组和基于多种假设的约束分组方法，以减少循环闭合中暂时静态对象的效果。随后，我们在包含许多动态对象的公共数据集中评估了我们的方法。最后，通过成功拒绝动态和暂时静态对象的效果，我们的测力量与其他最先进方法相比，我们的测力素具有有希望的性能得到证实。我们的代码可在https://github.com/url-kaist/dynavins上找到。

与其他标准摄像机相反，事件摄像机以完全不同的方式来解释世界。作为异步事件的集合。尽管事件摄像头的独特数据输出，但许多事件功能检测和跟踪算法通过绕开基于框架的数据表示表现出了重大进展。本文质疑这样做的需求，并提出了一种新颖的事件数据友好方法，该方法可以实现同时的特征检测和跟踪，称为基于事件聚类的检测和跟踪（ECDT）。我们的方法采用一种新颖的聚类方法，称为基于K-NN分类器的空间聚类和噪声应用程序（KCSCAN）的应用，用于聚类相邻的极性事件以检索事件轨迹。借助头部和尾部描述符匹配过程，事件群集，在不同的极性中重新出现，不断跟踪，从而拉长了功能轨道。由于我们在时空空间中的聚类方法，我们的方法可以自动求解功能检测和特征跟踪。此外，ECDT可以使用可调的时间窗口以任何频率提取功能轨道，这不会破坏原始事件数据的高时间分辨率。与最先进的方法相比，我们的方法可以达到30％的特征跟踪年龄，同时也具有与其大约等于其的低误差。

Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations, but incurs substantial training costs. In this paper, we propose a novel concept-based curriculum masking (CCM) method to efficiently pre-train a language model. CCM has two key differences from existing curriculum learning approaches to effectively reflect the nature of MLM. First, we introduce a carefully-designed linguistic difficulty criterion that evaluates the MLM difficulty of each token. Second, we construct a curriculum that gradually masks words related to the previously masked words by retrieving a knowledge graph. Experimental results show that CCM significantly improves pre-training efficiency. Specifically, the model trained with CCM shows comparative performance with the original BERT on the General Language Understanding Evaluation benchmark at half of the training cost.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

Most scanning LiDAR sensors generate a sequence of point clouds in real-time. While conventional 3D object detectors use a set of unordered LiDAR points acquired over a fixed time interval, recent studies have revealed that substantial performance improvement can be achieved by exploiting the spatio-temporal context present in a sequence of LiDAR point sets. In this paper, we propose a novel 3D object detection architecture, which can encode LiDAR point cloud sequences acquired by multiple successive scans. The encoding process of the point cloud sequence is performed on two different time scales. We first design a short-term motion-aware voxel encoding that captures the short-term temporal changes of point clouds driven by the motion of objects in each voxel. We also propose long-term motion-guided bird's eye view (BEV) feature enhancement that adaptively aligns and aggregates the BEV feature maps obtained by the short-term voxel encoding by utilizing the dynamic motion context inferred from the sequence of the feature maps. The experiments conducted on the public nuScenes benchmark demonstrate that the proposed 3D object detector offers significant improvements in performance compared to the baseline methods and that it sets a state-of-the-art performance for certain 3D object detection categories. Code is available at https://github.com/HYjhkoh/MGTANet.git

We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstacles. Our adaptation approach encourages the inherent regularities on the estimated maps to guide the agent to overcome the prevalent domain discrepancy in a novel environment. Specifically, we propose an efficient learning curriculum to handle the visual and dynamics corruptions in an online manner, self-supervised with pseudo clean maps generated by style transfer networks. Because the map-based representation provides spatial knowledge for the agent's policy, our formulation can deploy the pretrained policy networks from simulators in a new setting. We evaluate MoDA in various practical scenarios and show that our proposed method quickly enhances the agent's performance in downstream tasks including localization, mapping, exploration, and point-goal navigation.

有条件图像生成的最新方法受益于密集的监督，例如分割标签图，以实现高保真性。但是，很少探索使用密集的监督进行无条件的图像生成。在这里，我们探讨了密集监督在无条件生成中的功效，找到生成器特征图可以替代成本昂贵的语义标签图。从我们的经验证据来看，我们提出了一种新的生成器引导的鉴别剂正则化（GGDR），其中生成器的特征地图监督了歧视者在无条件生成中具有丰富的语义表示。具体而言，我们采用了一个U-NET架构进行鉴别器，该体系结构经过训练，可以预测发电机特征图作为输入的伪造图像。关于Mulitple数据集的广泛实验表明，我们的GGDR始终在定量和定性方面提高基线方法的性能。代码可从https://github.com/naver-ai/ggdr获得

心血管疾病（CVD）是全球死亡的第一大原因。尽管有越来越多的证据表明心房颤动（AF）与各种CVD有着密切的关联，但这种心律不齐通常是使用心电图（ECG）诊断的，这是一种无风险，无侵入性和具有成本效益的工具。在任何威胁生命的疾病/疾病发展之前，不断和远程监视受试者的心电图信息迅速诊断和及时对AF进行预处理的潜力。最终，可以降低CVD相关的死亡率。在此手稿中，展示了体现可穿戴心电图设备，移动应用程序和后端服务器的个性化医疗系统的设计和实施。该系统不断监视用户的心电图信息，以提供个性化的健康警告/反馈。用户能够通过该系统与他们的配对健康顾问进行远程诊断，干预措施等。已经评估了实施的可穿戴ECG设备，并显示出极好的一致性（CVRMS = 5.5％），可接受的一致性（CVRMS = CVRMS = CVRMS = 12.1％），可忽略不计的RR间隙错误（<1.4％）。为了提高可穿戴设备的电池寿命，提出了使用ECG信号的准周期特征来实现压缩的有损压缩模式。与公认的架构相比，它在压缩效率和失真方面优于其他模式，并在MIT-BIH数据库中以ECG信号的某个PRD或RMSE达到了至少2倍的Cr。为了在拟议系统中实现自动化AF诊断/筛查，开发了基于重新系统的AF检测器。对于2017年Physionet CINC挑战的ECG记录，该AF探测器获得了平均测试F1 = 85.10％和最佳测试F1 = 87.31％，表现优于最先进。

重要的理论工作已经确定，在特定的制度中，通过梯度下降训练的神经网络像内核方法一样行为。但是，在实践中，众所周知，神经网络非常优于其相关内核。在这项工作中，我们通过证明有一大批功能可以通过内核方法有效地学习，但是可以通过学习表示与相关的学习表示，可以轻松地学习这一差距。到目标任务。我们还证明了这些表示允许有效的转移学习，这在内核制度中是不可能的。具体而言，我们考虑学习多项式的问题，该问题仅取决于少数相关的方向，即$ f^\ star（x）= g（ux）$ withy $ u：\ r^d \ to \ r^r $ d \ gg r $。当$ f^\ star $的度数为$ p $时，众所周知，在内核制度中学习$ f^\ star $是必要的。我们的主要结果是，梯度下降学会了数据的表示，这仅取决于与$ f^\ star $相关的指示。这导致改进的样本复杂性为$ n \ asymp d^2 r + dr^p $。此外，在转移学习设置中，源和目标域中的数据分布共享相同的表示$ u $，但具有不同的多项式头部，我们表明，转移学习的流行启发式启发式启发式具有目标样本复杂性，独立于$ d $。