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GelBelt: A vision-Based Tactile Sensor for Continuous Sensing of Large Surfaces

IEEE ROBOTICS AND automation LETTERS 2025年第2期10卷 2016-2023页

作者： Mirzaee, Mohammad Amin Huang, Hung-Jui Yuan, Wenzhen Univ Illinois Champaign Urbana Champaign IL 61801 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

Scanning large-scale surfaces is widely demanded in surface reconstruction applications and detecting defects in industries' quality control and maintenance stages. Traditional vision-based tactile sensors have shown promising performance in high-resolution shape reconstruction while suffering limitations such as small sensing areas or susceptibility to damage when slid across surfaces, making them unsuitable for continuous sensing on large surfaces. To address these shortcomings, we introduce a novel vision-based tactile sensor designed for continuous surface sensing applications. Our design uses an elastomeric belt and two wheels to continuously scan the target surface. The proposed sensor showed promising results in both shape reconstruction and surface fusion, indicating its applicability. The dot product of the estimated and reference surface normal map is reported over the sensing area and for different scanning speeds. Results indicate that the proposed sensor can rapidly scan large-scale surfaces with high accuracy at speeds up to 45 mm/s.

关键词： Surface reconstruction Robot sensing systems Surface topography Sensors Surface treatment Belts Wheels Optical sensors Image reconstruction Elastomers computer vision for automation force and tactile sensing soft sensors and actuators

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Temporally Consistent Object 6D Pose Estimation for Robot Control

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IEEE ROBOTICS AND automation LETTERS 2025年第1期10卷 56-63页

作者： Zorina, Kateryna Priban, Vojtech Fourmy, Mederic Sivic, Josef Petrik, Vladimir Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague 16000 Czech Republic

Single-view RGB object pose estimators have reached a level of precision and efficiency that makes them good candidates for vision-based robot control. However, off-the-shelf methods lack temporal consistency and robustness that are mandatory for a stable feedback control. In this work, we develop a factor graph approach to enforce temporal consistency of the object pose estimates. In particular, the proposed approach: (i) incorporates object motion models, (ii) explicitly estimates the object pose measurement uncertainty, and (iii) integrates the above two components in an online optimization-based estimator. We demonstrate that with appropriate outlier rejection and smoothing using the proposed factor graph approach, we can significantly improve the results on standardized pose estimation benchmarks. We experimentally validate the stability of the proposed approach for a feedback-based robot control task in which the object is tracked by the camera attached to a torque controlled manipulator.

关键词： Cameras Robots Pose estimation Robot vision systems Robot control Probabilistic logic Uncertainty Smoothing methods Predictive models Computational modeling computer vision for automation visual tracking

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SDPENet: A Lightweight Spacecraft Pose Estimation Network With Discrete Euler Angle Probability Distribution

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IEEE ROBOTICS AND automation LETTERS 2025年第4期10卷 3086-3093页

作者： Zhou, Hang Yao, Lu She, Haoping Si, Weiyong Beijing Inst Technol Sch Aerosp & Engn Beijing 100081 Peoples R China Space Pioneer Beijing 100076 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

Utilizing deep learning techniques for spacecraft pose estimation enables using low-cost sensors like monocular cameras. However, the existing methods have drawbacks, such as complex models or low estimation accuracy. Therefore, this letter proposes the Spacecraft Discrete Pose Estimation Network (SDPENet). Firstly, we design a feature fusion network and a pose estimation head applicable to the spacecraft pose estimation task and devise the Spatial-Semantic Interaction Attention (SSIA) mechanism for feature fusion. Secondly, the discrete Euler angle probability distribution is proposed to represent the spacecraft attitude, significantly reducing the number of parameters while improving the accuracy. Finally, we put forward three data augmentation methods named CropAndPad, DropBlockSafe and Z-axis Rotation Safe to improve the performance of the network for the spacecraft pose estimation task. The experimental results demonstrate that, compared with the existing works, the errors in the spacecraft position and attitude estimated by SDPENet are reduced by 8.7%-83.1% and 31.7%-87.8% respectively, and simultaneously, the number of parameters is decreased by 33.3%-82.4%.

关键词： Space vehicles Pose estimation Accuracy Probability distribution Estimation Neck Computational modeling Cameras Solid modeling Convolutional codes AI-based methods computer vision for automation aerial systems: Applications

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3DGS-CD: 3D Gaussian Splatting-Based Change Detection for Physical Object Rearrangement

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IEEE ROBOTICS AND automation LETTERS 2025年第3期10卷 2662-2669页

作者： Lu, Ziqi Ye, Jianbo Leonard, John MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Leveraging 3DGS's novel view rendering and EfficientSAM's zero-shot segmentation capabilities, we detect 2D object-level changes, which are then associated and fused across views to estimate 3D change masks and object transformations. Our method can accurately identify changes in cluttered environments using sparse (as few as one) post-change images within as little as 18 s. It does not rely on depth input, user instructions, pre-defined object classes, or object models - An object is recognized simply if it has been re-arranged. Our approach is evaluated on both public and self-collected real-world datasets, achieving up to 14% higher accuracy and three orders of magnitude faster performance compared to the state-of-the-art radiance-field-based change detection method. This significant performance boost enables a broad range of downstream applications, where we highlight three key use cases: object reconstruction, robot workspace reset, and 3DGS model update.

关键词： Three-dimensional displays Image segmentation Cameras Rendering (computer graphics) Training Object recognition Neural radiance field Solid modeling Visualization Point cloud compression computer vision for automation mapping

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Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation

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IEEE ROBOTICS AND automation LETTERS 2025年第2期10卷 1281-1288页

作者： Li, Alan Schoellig, Angela P. Univ Toronto Inst Aerosp Studies Dynam Syst Lab Toronto ON M5S 1A1 Canada Tech Univ Munich TUM D-80333 Munich Germany

6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where objects may be textureless and in difficult poses, and occlusion between objects of the same type may cause confusion even in well-trained models. We propose a novel method of hard example synthesis that is model-agnostic, using existing simulators and the modeling of pose error in both the camera-to-object viewsphere and occlusion space. Through evaluation of the model performance with respect to the distribution of object poses and occlusions, we discover regions of high error and generate realistic training samples to specifically target these regions. With our training approach, we demonstrate an improvement in correct detection rate of up to 20% across several ROBI-dataset objects using state-of-the-art pose estimation models.

关键词： Training Pose estimation Training data Solid modeling Data mining Data models Cameras Space exploration computer vision Computational modeling computer vision for automation RGB-D perception deep learning for visual perceptio

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GARD: A Geometry-Informed and Uncertainty-Aware Baseline Method for Zero-Shot Roadside Monocular Object Detection

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IEEE ROBOTICS AND automation LETTERS 2025年第2期10卷 1297-1304页

作者： Peng, Yuru Wang, Beibei Yu, Zijian Zhang, Lu Ji, Jianmin Zhang, Yu Zhang, Yanyong Univ Sci & Technol China Inst Adv Technol Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230026 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Suzhou Shuzhi Technol Grp Co Ltd Suzhou 215000 Peoples R China

Roadside camera-based perception methods are in high demand for developing efficient vehicle-infrastructure collaborative perception systems. By focusing on object-level depth prediction, we explore the potential benefits of integrating environmental priors into such systems and propose a geometry-based roadside per-object depth estimation algorithm dubbed GARD. The proposed method capitalizes on the inherent geometric properties of the pinhole camera model to derive depth as well as 3D positions for given 2D targets in roadside-view images, alleviating the need for computationally intensive end-to-end learning architectures for monocular 3D detection. Using only a pre-trained 2D detection model, our approach does not require vast amounts of scene-specific training data and shows superior generalization abilities across varying environments and camera setups, making it a practical and cost-effective solution for monocular 3D object detection.

关键词： Three-dimensional displays Cameras Roads Depth measurement Object detection Estimation Feature extraction Detectors Deep learning Accuracy computer vision for automation intelligent transport systems object detection

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GAP-RL: Grasps as Points for RL Towards Dynamic Object Grasping

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IEEE ROBOTICS AND automation LETTERS 2025年第1期10卷 40-47页

作者： Xie, Pengwei Chen, Siang Chen, Qianrun Tang, Wei Hu, Dingchang Dai, Yixiang Chen, Rui Wang, Guijin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Shanghai AI Lab Shanghai 200232 Peoples R China Vanderbilt Univ Engn Sch Risk Reliabil & Resilience Engn Nashville TN 37212 USA Tsinghua Univ Shenzhen Int Grad Sch Dept Elect Engn Shenzhen 518071 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

Dynamic grasping of moving objects in complex, continuous motion scenarios remains challenging. Reinforcement Learning (RL) has been applied in various robotic manipulation tasks, benefiting from its closed-loop property. However, existing RL-based methods do not fully explore the potential for enhancing visual representations. In this letter, we propose a novel framework called Grasps As Points for RL (GAP-RL) to effectively and reliably grasp moving objects. By implementing a fast region-based grasp detector, we build a Grasp Encoder by transforming 6D grasp poses into Gaussian points and extracting grasp features as a higher-level abstraction than the original object point features. Additionally, we develop a Graspable Region Explorer for real-world deployment, which searches for consistent graspable regions, enabling smoother grasp generation and stable policy execution. To assess the performance fairly, we construct a simulated dynamic grasping benchmark involving objects with various complex motions. Experiment results demonstrate that our method effectively generalizes to novel objects and unseen dynamic motions compared with other baselines. Real-world experiments further validate the framework's sim-to-real transferability.

关键词： Grasping Dynamics Feature extraction Robots Three-dimensional displays Target tracking Visualization Point cloud compression Heuristic algorithms Benchmark testing Deep learning in grasping and manipulation reinforcement learning (RL) computer vision for automation

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H-PCC: Point Cloud Compression With Hybrid Mode Selection and Content Adaptive Down-Sampling

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IEEE ROBOTICS AND automation LETTERS 2025年第4期10卷 3054-3061页

作者： Liu, Bowen Chen, Yu Wang, Boyang Yang, Mingyu Kim, Hun-Seok Univ Michigan EECS Dept Ann Arbor MI 48109 USA

LiDAR sensors are integral to autonomous driving and augmented reality applications, providing essential depth information. However, managing the substantial volume of LiDAR point cloud data is crucial for practical application, necessitating efficient compression algorithms. Similar to other data compression domains, point cloud compression aims to spatially and temporally decorrelate information while preserving reconstruction quality. Addressing real-world challenges such as sensor pose retrieval inaccuracies or motion estimation failures, we introduce H-PCC, a hybrid LiDAR data compression pipeline that combines a range image prediction-based dynamic compression method with a novel point clustering octree-based static method. Additionally, we demonstrate that our content adaptive down-sampling technique using associated camera images can significantly enhance the LiDAR data compression rate with minimal impact on the accuracy of machine perception tasks. Extensive evaluations on the KITTI and Oxford datasets, using both traditional perceptual quality metrics and machine vision tasks, show that H-PCC surpasses state-of-the-art LiDAR data compression methods.

关键词： Point cloud compression Laser radar Image coding Three-dimensional displays Data compression Sensors Measurement by laser beam Octrees Entropy Image reconstruction computer vision for automation deep learning for visual perception sensor fusion

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MetaMVUC: Active Learning for Sample-Efficient Sim-to-Real Domain Adaptation in Robotic Grasping

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IEEE ROBOTICS AND automation LETTERS 2025年第4期10卷 3644-3651页

作者： Gilles, Maximilian Furmans, Kai Rayyes, Rania Karlsruhe Inst Technol KIT D-76131 Karlsruhe Germany

Learning-based robotic grasping systems typically rely on large-scale datasets for training. However, collecting such datasets in the real-world is both costly and time-consuming. Synthetic data generation data is a cost-effective alternative, but models trained solely on synthetic data often struggle with zero-shot real-world performance due to the large domain gap between synthetic and real-world data. To address this challenge of dataset costs against model performance, we propose an active learning framework designed for fast and sample-efficient sim-to-real domain adaptation. Our proposed learning framework uses synthetic data as initial knowledge base and incrementally adapts to the target data domain by selecting the most informative real-world data samples for further model training. For this purpose, we propose a novel, hybrid query strategy, MetaMVUC, which leverages multi-view uncertainty and metadata diversity. MetaMVUC assesses model uncertainty by comparing model predictions across multiple viewpoints, identifying samples with the highest uncertainty. Additionally, since robots in industry or logistics often operate in environments rich in metadata, MetaMVUC leverages this information to ensure diverse and well-distributed sample selection. Experimental results demonstrate the effectiveness of our proposed learning framework. With a limited annotation budget of 16 samples, a robot trained using MetaMVUC achieves a successful grasp rate of 87.7%. Increasing the budget to 40 samples improves grasp performance to 96.7%, outperforming the zero-shot sim-to-real by 17.4% and 26.4%, respectively.

关键词： Uncertainty Robots Adaptation models Grasping Cameras Training Synthetic data Metadata Costs Robot vision systems Deep learning in grasping and manipulation computer vision for automation perception for grasping and manipulation

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Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensification

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IEEE ROBOTICS AND automation LETTERS 2025年第5期10卷 4548-4555页

作者： Yao, Yasuhiro Ishikawa, Ryoichi Oishi, Takeshi Univ Tokyo Inst Ind Sci Tokyo 1538505 Japan

We present a real-time, non-learning depth estimation method that fuses Light Detection and Ranging (LiDAR) data with stereo camera input. Our approach comprises three key techniques: Semi-Global Matching (SGM) stereo with Discrete Disparity-matching Cost (DDC), semidensification of LiDAR disparity, and a consistency check that combines stereo images and LiDAR data. Each of these components is designed for parallelization on a GPU to realize real-time performance. When it was evaluated on the KITTI dataset, the proposed method achieved an error rate of 2.79%, outperforming the previous state-of-the-art real-time stereo-LiDAR fusion method, which had an error rate of 3.05%. Furthermore, we tested the proposed method in various scenarios, including different LiDAR point densities, varying weather conditions, and indoor environments, to demonstrate its high adaptability. We believe that the real-time and non-learning nature of our method makes it highly practical for applications in robotics and automation.

关键词： Laser radar Costs Real-time systems Cameras Learning systems Accuracy Depth measurement Training Sensor fusion Robot vision systems computer vision for automation depth completion range sensing sensor fusion stereo matching

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