检索结果-内蒙古大学图书馆

Uncertainty-Aware Real-Time Visual Anomaly Detection With Conformal Prediction in Dynamic Indoor Environments

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4468-4475页

作者： Saboury, Arya Uyguroglu, Mustafa Kemal Eastern Mediterranean Univ Dept Elect & Elect Engn TR-99628 Mersin Turkiye

This letter presents an efficient visual anomaly detection framework designed for safe autonomous navigation in dynamic indoor environments, such as university hallways. The approach employs an unsupervised autoencoder method within deep learning to model regular environmental patterns and detect anomalies as deviations in the embedding space. To enhance reliability and safety, the system integrates a statistical framework, conformal prediction, that provides uncertainty quantification with probabilistic guarantees. The proposed solution has been deployed on a real-time robotic platform, demonstrating efficient performance under resource-constrained conditions. Extensive hyperparameter optimization ensures the model remains dynamic and adaptable to changes, while rigorous evaluations confirm its effectiveness in anomaly detection. By addressing challenges related to real-time processing and hardware limitations, this work advances the state-of-the-art in autonomous anomaly detection. The probabilistic insights offered by this framework strengthen operational safety and pave the way for future developments, such as richer sensor fusion and advanced learning paradigms. This research highlights the potential of uncertainty-aware deep learning to enhance safety monitoring frameworks, thereby enabling the development of more reliable and intelligent autonomous systems for real-world applications.

关键词： Anomaly detection Autoencoders Image reconstruction Robots Training Safety Uncertainty Reliability Real-time systems Probabilistic logic Deep learning for visual perception probability and statistical methods anomaly detection conformal prediction

来源：评论

学校读者我要写书评

暂无评论

Generalized Maximum Likelihood Estimation for Perspective-n-Point Problem

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1752-1759页

作者： Zhan, Tian Xu, Chunfeng Zhang, Cheng Zhu, Ke Beijing Inst Technol Sch Aerosp & Engn Beijing 100811 Peoples R China

The Perspective-n-Point (PnP) problem has been widely studied in the literature and applied in various vision-based pose estimation scenarios. However, most existing methods ignore the anisotropy uncertainty of observations, as demonstrated in several real-world datasets in this letter. This oversight may lead to suboptimal and inaccurate estimation, particularly in the presence of noisy observations. To this end, we propose a generalized maximum likelihood PnP solver, named GMLPnP, that minimizes the determinant criterion by iterating the generalized least squares procedure to estimate the pose and uncertainty simultaneously. Further, the proposed method is decoupled from the camera model. Results of synthetic and real experiments show that our method achieves better accuracy in common pose estimation scenarios, GMLPnP improves rotation/translation accuracy by 4.7%/2.0% on TUM-RGBD and 18.6%/18.4% on KITTI-360 dataset compared to the best baseline. It is more accurate under very noisy observations in a vision-based UAV localization task, outperforming the best baseline by 34.4% in translation estimation accuracy.

关键词： Cameras Uncertainty Noise Maximum likelihood estimation Covariance matrices Accuracy Noise measurement Location awareness Anisotropic Pose estimation Localization vision-based navigation probability and statistical methods

来源：评论

学校读者我要写书评

暂无评论

Scenario-based motion planning with bounded probability of collision

引用

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2025年

作者： de Groot, Oscar Ferranti, Laura Gavrila, Dariu M. Alonso-Mora, Javier Delft Univ Technol Dept Cognit Robot Mekelweg 2 NL-2628 CD Delft Netherlands

Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Optimization-based planners typically avoid humans through collision avoidance chance constraints. This allows the planner to optimize performance while guaranteeing probabilistic safety. However, existing real-time methods do not consider the actual probability of collision for the planned trajectory but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Out of sampled realizations of human motion, we identify which cases affect the optimization. This allows us to certify the planned trajectory in real-time. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans.

关键词： Motion and path planning optimization and optimal control collision avoidance probability and statistical methods

来源：评论

学校读者我要写书评

暂无评论

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 2096-2103页

作者： Saavedra-Ruiz, Miguel Parkison, Steven A. Arora, Ria Forbes, James Richard Paull, Liam Univ Montreal Dept Comp Sci & Operat Res Montreal PQ H3T 1N8 Canada Mila Quebec AI Inst Montreal PQ H2S 3H1 Canada McGill Univ Dept Mech Engn Montreal PQ H3A 2K6 Canada

Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are all unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This letter introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's superior performance compared with established nonparametric filtering methods across a range of simulated and real-world localization tasks.

关键词： Sensor fusion probability and statistical methods localization Sensor fusion probability and statistical methods localization

来源：评论

学校读者我要写书评

暂无评论

Combining the SLAM back and front ends with a joint vector-set distribution

引用

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2025年

作者： Inostroza, Felipe Adams, Martin Univ Chile Adv Min Technol Ctr AMTC Av Tupper 2007 Santiago 8370451 Chile Univ Chile Dept Elect Engn Av Tupper 2007 Santiago 8370451 Chile

The joint optimization of map management and map feature to measurement association, together with the trajectory and map states, within a single, unified, Bayesian, feature-based, simultaneous localization and mapping (SLAM) solution is addressed in this article. Remarkable progress in feature-based SLAM has been made in which, given data association, the SLAM problem can be solved by use of nonlinear least squares solvers, often referred to as the SLAM back-end. These methods rely on external methods to solve both the data association and map management problems, which are collectively incorporated into the SLAM front-end. SLAM convergence failures are common when these front-end routines fail, particularly when feature detection uncertainty increases. Therefore, this article introduces Joint, Vector-Set SLAM (JVS-SLAM), utilizing Bayes theorem to solve feature to measurement association, map management, and SLAM itself jointly, thus combining the SLAM back and front ends. Results will demonstrate equivalent or superior SLAM performance to state-of-the-art solutions, under varying odometry, spatial and detection measurement uncertainties, without reliance on data association decisions. Results are based on both simulations and the challenging EuRoC data set, in which a drone undergoing high accelerations, equipped with a stereo camera, performs SLAM. Since JVS-SLAM jointly provides a solution to the map feature to measurement association problem, its computational complexity is comparable with multi-hypothesis based solutions. Parallels between state-of-the-art map management and feature to measurement association methods and the detection statistics used within JVS-SLAM will be examined, with a view to reducing its complexity in the future.

关键词： Localization and mapping probability and statistical methods theoretical foundations sensor-based navigation vision and sensor-based control

来源：评论

学校读者我要写书评

暂无评论

Center-of-Mass Location Estimation for Improving the Adaptability of Grasping Failure Recovery Policy

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Wang, Yue Li, Tiemin Jiang, Yao Tsinghua Univ Inst Mfg Engn Dept Mech Engn Beijing 100084 Peoples R China

Vision-based grasping policies may fail on unknown center-of-mass objects (UCMOs) due to the inaccurate estimation of center of mass (CoM). While force and tactile sensors, as well as reinforcement learning (RL) methods, have been utilized to recover from grasping failures, the adaptability is limited to single-axis and simple multiaxis objects (objects with multiple branches). To enhance the adaptability of the grasping failure recovery policy for UCMOs, we propose explicitly estimating the CoM using a novel Bayesian method. Additionally, we introduce a statistical description of task performance and derive a unified metric for evaluating the reliability and efficiency of grasping policies. Compared to the state-of-the-art RL-based method, our approach demonstrates similar efficiency and higher reliability on single-axis objects, with an average success rate of 93.6% and an average number of attempts of 4.39. Moreover, on complexmultiaxis objects, our method outperforms the RL-based method (92.0% versus 64.0% and 3.96 versus 6.36). The proposed method improves the adaptability of the grasping failure recovery policy and requires no pretraining.

关键词： Grasping Estimation Measurement Reliability Grippers Bayes methods Trajectory Training Torque Tactile sensors Perception for grasping probability and statistical methods robotic grasping

来源：评论

学校读者我要写书评

暂无评论

Sensor Query Schedule and Sensor Noise Covariances for Accuracy-constrained Trajectory Estimation

引用

IEEE Robotics and Automation Letters 2025年第7期10卷 6983-6990页

作者： Goudar, Abhishek Schoellig, Angela P. Technical University of Munich Learning Systems and Robotics Lab Germany University of Toronto Institute for Aerospace Studies Canada University of Toronto Robotics Institute Munich Institute of Robotics and Machine Intelligence (MIRMI) Vector Institute for Artificial Intelligence Canada

Trajectory estimation involves determining the trajectory of a mobile robot by combining prior knowledge about its dynamic model with noisy observations of its state obtained using sensors. The accuracy of such a procedure is dictated by the system model fidelity and the sensor parameters, such as the accuracy of the sensor (as represented by its noise covariance) and the rate at which it can generate observations, referred to as the sensor query schedule. Intuitively, high-rate measurements from accurate sensors lead to accurate trajectory estimation. However, cost and resource constraints limit the sensor accuracy and its measurement rate. Our work's novel contribution is the estimation of sensor schedules and sensor covariances necessary to achieve a specific estimation accuracy. Concretely, we focus on estimating: (i) the rate or schedule with which a sensor of known covariance must generate measurements to achieve specific estimation accuracy, and alternatively, (ii) the sensor covariance necessary to achieve specific estimation accuracy for a given sensor update rate. We formulate the problem of estimating these sensor parameters as semidefinite programs, which can be solved by off-the-shelf solvers. We validate our approach in simulation and real experiments by showing that the sensor schedules and the sensor covariances calculated using our proposed method achieve the desired trajectory estimation accuracy. Our method also identifies scenarios where certain estimation accuracy is unachievable with the given system and sensor characteristics. © 2016 IEEE.

关键词： Localization Optimization and Optimal control probability and statistical methods

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第12期9卷 11234-11241页

作者： Hatleskog, Johan Alexis, Kostas Norwegian Univ Sci & Technol Dept Engn Cybernet N-7491 Trondheim Norway Cognite AS N-1366 Lysaker Norway Norwegian Univ Sci & Technol Dept Engn Cybernet N-7491 Trondheim Norway

Degeneracies arising from uninformative geometry are known to deteriorate LiDAR-based localization and mapping. This work introduces a new probabilistic method to detect and mitigate the effect of degeneracies in point-to-plane error minimization. The noise on the Hessian of the point-to-plane optimization problem is characterized by the noise on points and surface normals used in its construction. We exploit this characterization to quantify the probability of a direction being degenerate. The degeneracy-detection procedure is used in a new real-time degeneracy-aware iterative closest point algorithm for LiDAR registration, in which we smoothly attenuate updates in degenerate directions. The method's parameters are selected based on the noise characteristics provided in the LiDAR's datasheet. We validate the approach in four real-world experiments, demonstrating that it outperforms state-of-the-art methods at detecting and mitigating the adverse effects of degeneracies.

关键词： Noise Cost function Probabilistic logic Laser radar Real-time systems Perturbation methods Noise measurement Minimization Eigenvalues and eigenfunctions Vectors SLAM probability and statistical methods

来源：评论

学校读者我要写书评

暂无评论

An Adaptable, Probabilistic, Next-Best View Algorithm for Reconstruction of Unknown 3-D Objects

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2017年第3期2卷 1540-1547页

作者： Daudelin, Jonathan Campbell, Mark Cornell Univ Sch Mech & Aerosp Engn Robot Dept Ithaca NY 14850 USA

Autonomous mobile robots perform many tasks, such as grasping and inspection, that may require complete models of three-dimensional (3-D) objects in the environment. If little or no knowledge about an object is known a priori, the robot must take sensor measurements from strategically determined viewpoints in order to reconstruct a 3-D model of the object. We propose an autonomous object reconstruction approach for mobile robots that is very general, with no assumptions about object shape or size, such as a bounding box or predetermined set of candidate viewpoints. A probabilistic, volumetric method for determining the optimal next-best view is developed based on a partial model of a 3-D object of unknown shape and size. The proposed method integrates an object probability characteristic to determine sensor views that incrementally reconstruct a 3-D model of the object. Experiments in simulation and on a real-world robot validate the work and compare it to the state of the art.

关键词： Autonomous agents probability and statistical methods motion and path planning

来源：评论

学校读者我要写书评

暂无评论

PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning From Demonstration

引用

IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 2868-2887页

作者： Ruan, Sipu Liu, Weixiao Wang, Xiaoli Meng, Xin Chirikjian, Gregory S. Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore Johns Hopkins Univ Dept Mech Engn Baltimore 21218 MD USA Univ Delaware Dept Mech Engn Newark NJ 19716 USA

This article proposes a Learning-from-Demonstration (LfD) method using probability densities on the workspaces of robot manipulators. The method, named PRobabilistically-Informed Motion Primitives (PRIMP), learns the probability distribution of the end effector trajectories in the 6-D workspace that includes both positions and orientations. It is able to adapt to new situations such as novel via points with uncertainty and a change of viewing frame. The method itself is robot-agnostic, in that the learned distribution can be transferred to another robot with the adaptation to its workspace density. Workspace-STOMP, a new version of the existing STOMP motion planner, is also introduced, which can be used as a postprocess to improve the performance of PRIMP and any other reachability-based LfD method. The combination of PRIMP and Workspace-STOMP can further help the robot avoid novel obstacles that are not present during the demonstration process. The proposed methods are evaluated with several sets of benchmark experiments. PRIMP runs more than five times faster than existing state-of-the-art methods while generalizing trajectories more than twice as close to both the demonstrations and novel desired poses. They are then combined with our lab's robot imagination method that learns object affordances, illustrating the applicability to learn tool use through physical experiments.

关键词： Learning from demonstration (LfD) motion and path planning probability and statistical methods service robots

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：