移动机器人的自主导航是各个行业的重要任务。传感器数据对于确保安全可靠的导航至关重要。但是，传感器观察通常受到不同因素的限制。想象力可以帮助在只有有限的传感器观察的危险或未知情况下增强视图和帮助导航。在本文中，我们提出了基于2D语义激光扫描数据的想象增强导航。该系统包含一个想象模块，该模块可以预测对象的整个占用区域。想象模块使用来自2D模拟器的收集培训数据集以监督方式进行训练。训练了四个不同的想象模型，并评估了想象结果。随后，将想象结果集成到本地和全球成本图中，以使导航程序受益。该方法在三个不同的测试地图上进行了验证，每个地图有七个不同的路径。质量和数字结果表明，具有想象力模块的试剂可以生成更可靠的路径，而无需在物体下方经过，其成本更长，速度较慢。

援助机器人在物流和人类援助等各个行业中广泛关注。在拥挤的环境（例如机场或火车站）携带重量或货物的指导或关注人类的任务仍然是一个空旷的问题。在这些用例中，机器人不仅需要与人类智能互动，而且需要在人群中安全地进行互动。因此，尤其是高度动态的环境，由于人类的挥发性行为模式和不可预测的运动，构成了巨大的挑战。在本文中，我们提出了一种基于深入的学习媒介，用于在拥挤的环境中进行人类引导和遵守任务。因此，我们合并了语义信息，以向代理提供高级信息，例如人类，安全模型和班级类型的社会状态。我们在没有语义信息的情况下根据基准方法评估了我们的建议方法，并证明了导航的安全性和鲁棒性增强。此外，我们证明了代理可以学会将其行为适应人类，从而大大改善了人类机器人的相互作用。

对于移动机器人来说，自主行驶安全性的能力，尤其是在动态环境中的能力至关重要。近年来，DRL方法在避免动态障碍物方面表现出了出色的表现。但是，这些基于学习的方法通常是在专门设计的仿真环境中开发的，并且很难针对传统的计划方法进行测试。此外，这些方法将这些方法的集成和部署到真正的机器人平台中尚未完全解决。在本文中，我们介绍了Arena-Bench，这是一套基准套件，可在3D环境中在不同机器人平台上进行训练，测试和评估导航计划者。它提供了设计和生成高度动态评估世界，场景和自动导航任务的工具，并已完全集成到机器人操作系统中。为了展示我们套件的功能，我们在平台上培训了DRL代理，并将其与各种相关指标上的各种现有基于模型和学习的导航方法进行了比较。最后，我们将方法部署到了真实的机器人方面，并证明了结果的可重复性。该代码可在github.com/ignc-research/arena-bench上公开获得。

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

Over the years, sequential Monte Carlo (SMC) and, equivalently, particle filter (PF) theory has gained substantial attention from researchers. However, the performance of the resampling methodology, also known as offspring selection, has not advanced recently. We propose two deterministic offspring selection methods, which strive to minimize the Kullback-Leibler (KL) divergence and the total variation (TV) distance, respectively, between the particle distribution prior and subsequent to the offspring selection. By reducing the statistical distance between the selected offspring and the joint distribution, we obtain a heuristic search procedure that performs superior to a maximum likelihood search in precisely those contexts where the latter performs better than an SMC. For SMC and particle Markov chain Monte Carlo (pMCMC), our proposed offspring selection methods always outperform or compare favorably with the two state-of-the-art resampling schemes on two models commonly used as benchmarks from the literature.

Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perception made tremendous progress in recent years, but face limitations under adverse weather conditions. To leverage the full potential of multi-modal sensor suites, radar sensors are essential for safety critical tasks and are already installed in most new vehicles today. In this paper, we address the problem of semantic segmentation of moving objects in radar point clouds to enhance the perception of the environment with another sensor modality. Instead of aggregating multiple scans to densify the point clouds, we propose a novel approach based on the self-attention mechanism to accurately perform sparse, single-scan segmentation. Our approach, called Gaussian Radar Transformer, includes the newly introduced Gaussian transformer layer, which replaces the softmax normalization by a Gaussian function to decouple the contribution of individual points. To tackle the challenge of the transformer to capture long-range dependencies, we propose our attentive up- and downsampling modules to enlarge the receptive field and capture strong spatial relations. We compare our approach to other state-of-the-art methods on the RadarScenes data set and show superior segmentation quality in diverse environments, even without exploiting temporal information.

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework facilitating the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations.

A reliable pose estimator robust to environmental disturbances is desirable for mobile robots. To this end, inertial measurement units (IMUs) play an important role because they can perceive the full motion state of the vehicle independently. However, it suffers from accumulative error due to inherent noise and bias instability, especially for low-cost sensors. In our previous studies on Wheel-INS \cite{niu2021, wu2021}, we proposed to limit the error drift of the pure inertial navigation system (INS) by mounting an IMU to the wheel of the robot to take advantage of rotation modulation. However, it still drifted over a long period of time due to the lack of external correction signals. In this letter, we propose to exploit the environmental perception ability of Wheel-INS to achieve simultaneous localization and mapping (SLAM) with only one IMU. To be specific, we use the road bank angles (mirrored by the robot roll angles estimated by Wheel-INS) as terrain features to enable the loop closure with a Rao-Blackwellized particle filter. The road bank angle is sampled and stored according to the robot position in the grid maps maintained by the particles. The weights of the particles are updated according to the difference between the currently estimated roll sequence and the terrain map. Field experiments suggest the feasibility of the idea to perform SLAM in Wheel-INS using the robot roll angle estimates. In addition, the positioning accuracy is improved significantly (more than 30\%) over Wheel-INS. Source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-SLAM).