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arXiv 2025年

作者： Nazib, Abdullah Fookes, Clinton Perrin, Dimitri School of Computer Science Queensland University of Technology BrisbaneQLD Australia School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD Australia Centre for Data Science Queensland University of Technology BrisbaneQLD Australia

We developed a generative patch based 3D image registration model that can register very high resolution images obtained from a biochemical process name tissue clearing. Tissue clearing process removes lipids and fats from the tissue and make the tissue transparent. When cleared tissues are imaged with Light-sheet fluorescent microscopy, the resulting images give a clear window to the cellular activities and dynamics inside the *** the images obtained are very rich with cellular information and hence their resolution is extremely high (eg .2560 × 2160 × 676). Analyzing images with such high resolution is a difficult task for any image analysis *** registration is a common step in image analysis pipeline when comparison between images are required. Traditional image registration methods fail to register images with such extant. In this paper we addressed this very high resolution image registration issue by proposing a patch-based generative network named InvGAN. Our proposed network can register very high resolution tissue cleared images. The tissue cleared dataset used in this paper are obtained from a tissue clearing protocol named CUBIC. We compared our method both with traditional and deep-learning based registration *** different versions of CUBIC dataset are used, representing two different resolutions 25% and 100% respectively. Experiments on two different resolutions clearly show the impact of resolution on the registration quality. At 25% resolution, our method achieves comparable registration accuracy with very short time (7 minutes approximately). At 100% resolution, most of the traditional registration methods fail except Elastix registration *** takes 28 hours to register where proposed InvGAN takes only 10 minutes. © 2025, CC BY.

关键词： Image registration

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Cooperative Multi-source Data Trading

Cooperative Multi-source Data Trading

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Cheng, Jin Ding, Ningning Lui, John C. S. Huang, Jianwei The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Hong Kong University of Science and Technology Data Science and Analytics Thrust Information Hub Guangzhou China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen China

ISBN: (纸本)9798350351255

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers' cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data's utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers' revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller's profit by up to 46.32% compared to traditional data trading methods in the current data market. © 2024 IEEE.

关键词： Marketplaces

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PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration

arXiv

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arXiv 2025年

作者： Baek, Seungjae Moon, Brady Kim, Seungchan Cao, Muqing Ho, Cherie Scherer, Sebastian Jeon, Jeong hwan Ulsan National Institute of Science and Technology Ulsan Korea Republic of Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration. Copyright © 2025, The Authors. All rights reserved.

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Semantic-guided Image Feature Matching for Agricultural Robots 4

Semantic-guided Image Feature Matching for Agricultural Robo...

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4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Yu, Zhenghong Zhu, Xiuli Zhou, Huabing College of Robotics Guangdong Polytechnic of Science and Technology China School of Computer Science and Engineering Wuhan Institute of Technology China

ISBN: (纸本)9781450384087

Image feature matching is one of the most important basic techniques for agricultural robots. This task has been tried, but because of the complexity of the image, especially the non-rigid transformations, the performance is still far from satisfactory. This paper proposed a semantic-guided feature matching method for both agricultural mobile robot and UAV via semantic segmentation and probabilistic inference. Given an original image pair, a learning-based method, which is very active to the challenging variation of outdoor luminosity, is introduced firstly to produce a sematic image pair. This step shifts the robot's focus to the task itself, while improving matching efficiency at the same time. Then, based on the characteristic similarity of the ROI region of the semantic image pair, a series of putative correspondences are generated. Next, the focuses on eliminating outliers from that series while deducting the transformation, where this procedure was developed on a maximum likelihood Bayesian model. The paper has also applied three effective regularization techniques to the correspondence, which helped to come up with the best solution. Finally, as the problem was solved by EM algorithm and extensive experiments on those images taken from the real farmland, it is safe to say that the accurate results of this method are superior to other state-of-the-art methods. © 2021 ACM.

关键词： Mobile robots

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN computer science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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CT-MLO: Voxel-Based Multi-LiDAR Odometry Using Continuous-Time Kalman Filter

CT-MLO: Voxel-Based Multi-LiDAR Odometry Using Continuous-Ti...

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International Conference on Control, Automation, robotics and Vision (ICARCV)

作者： Hongming Shen Zhenyu Wu Wei Wang Qiyang Lyu Huiqin Zhou Yeqing Zhu Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science & Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In recent years, LiDAR-based localization and mapping methods have achieved significant progress thanks to their reliable and real-time localization capability. However, single LiDAR odometry often faces hardware failures and degradation in practical scenarios, and the continuous-time measurement characteristic is constantly neglected by existing LiDAR odometry. This motivates us to develop a continuous-time Multi-LiDAR Odometry (MLO) method, namely CT-MLO, which can realize accurate and real-time state estimation using multi-LiDAR measurements through a continuous-time perspective. Due to the advantageous continuous-time formulation, each LiDAR point in a point stream can query the corresponding continuous-time trajectory within its time instants. Additionally, a decentralized multi-LiDAR synchronization scheme is devised to combine points from separate LiDARs into a single point cloud without the need for primary LiDAR assignment. With the detailed derivation of the analytic Jacobians for continuous-time LiDAR observation, the proposed method integrates synchronization, continuous-time estimation, and voxel map management within a Kalman filter framework, which can achieve real-time state estimation with only a few linear iterations. The effectiveness of the proposed method is demonstrated through various scenarios, including public datasets and real-world autonomous driving experiments. The results demonstrate that the proposed CT-MLO can achieve high-accuracy continuous-time state estimations in real-time and is demonstratively competitive compared to other State-of-the-Art (SOTA) methods.

关键词： Point cloud compression Location awareness Jacobian matrices Laser radar Accuracy Real-time systems Synchronization Odometry Kalman filters State estimation

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QueryAdapter: Rapid Adaptation of Vision-Language Models in Response to Natural Language Queries

arXiv

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arXiv 2025年

作者： Chapman, Nicolas Harvey Dayoub, Feras Browne, Will Lehnert, Christopher School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia School of Computer Science Australian Institute of Machine Learning University of Adelaide Adelaide Australia

A domain shift exists between the large-scale, internet data used to train a Vision-Language Model (VLM) and the raw image streams collected by a robot. Existing adaptation strategies require the definition of a closed-set of classes, which is impractical for a robot that must respond to diverse natural language queries. In response, we present QueryAdapter;a novel framework for rapidly adapting a pre-trained VLM in response to a natural language query. QueryAdapter leverages unlabelled data collected during previous deployments to align VLM features with semantic classes related to the query. By optimising learnable prompt tokens and actively selecting objects for training, an adapted model can be produced in a matter of minutes. We also explore how objects unrelated to the query should be dealt with when using real-world data for adaptation. In turn, we propose the use of object captions as negative class labels, helping to produce better calibrated confidence scores during adaptation. Extensive experiments on ScanNet++ demonstrate that QueryAdapter significantly enhances object retrieval performance compared to state-of-the-art unsupervised VLM adapters and 3D scene graph methods. Furthermore, the approach exhibits robust generalization to abstract affordance queries and other datasets, such as Ego4D. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control

arXiv

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arXiv 2025年

作者： Wang, Haochen Shi, Zhiwei Zhu, Chengxi Qiao, Yafei Zhang, Cheng Yang, Fan Ren, Pengjie Lu, Lan Xuan, Dong School of Computer Science and Technology Shandong University China Robotics Institute Carnegie Mellon University United States DeepCode Robotics China Department of Sports Shanghai Jiao Tong University China

Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity and ensure the safety and stability for agile badminton robot control. In this paper, we introduce Hamlet, a novel hybrid control system for agile badminton robots. Specifically, we propose a model-based strategy for chassis locomotion which provides a base for arm policy. We introduce a physics-informed "IL+RL" training framework for learning-based arm policy. In this train framework, a model-based strategy with privileged information is used to guide arm policy training during both IL and RL phases. In addition, we train the critic model during IL phase to alleviate the performance drop issue when transitioning from IL to RL. We present results on our self-engineered badminton robot, achieving 94.5% success rate against the serving machine and 90.7% success rate against human players. Our system can be easily generalized to other agile mobile manipulation tasks such as agile catching and table tennis. Our project website: https://***/HAMLET/. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection

arXiv

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arXiv 2024年

作者： Leng, Kai Zhang, Zhijie Liu, Jie Sui, Wei Boukhers, Zeyd Yang, Cong Li, Zhijun School of Computer Science and Technology Harbin Institute of Technology China School of Future Science and Engineering Soochow Universit China Fraunhofer Institute for Applied Information Technolog Sankt Augustin Germany Horizon Robotics

Edge detection is a fundamental technique in various computer vision tasks. Edges are indeed effectively delineated by pixel discontinuity and can offer reliable structural information even in texture-less areas. State-of-the-art heavily relies on pixel-wise annotations, which are labor-intensive and subject to inconsistencies when acquired manually. In this work, we propose a novel self-supervised approach for edge detection that employs a multilevel, multi-homography technique to transfer annotations from synthetic to real-world datasets. To fully leverage the generated edge annotations, we developed SuperEdge, a streamlined yet efficient model capable of concurrently extracting edges at pixel-level and object-level granularity. Thanks to self-supervised training, our method eliminates the dependency on manual annotated edge labels, thereby enhancing its generalizability across diverse datasets. Comparative evaluations reveal that SuperEdge advances edge detection, demonstrating improvements of 4.9% in ODS and 3.3% in OIS over the existing STEdge method on BIPEDv2. Copyright © 2024, The Authors. All rights reserved.

关键词： Edge detection

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Finding Control Invariant Sets via Lipschitz Constants of Linear Programs

arXiv

引用

arXiv 2024年

作者： Vahs, Matti Han, Shaohang Tumova, Jana Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

Control invariant sets play an important role in safety-critical control and find broad application in numerous fields such as obstacle avoidance for mobile robots. However, finding valid control invariant sets of dynamical systems under input limitations is notoriously difficult. We present an approach to safely expand an initial set while always guaranteeing that the set is control invariant. Specifically, we define an expansion law for the boundary of a set and check for control invariance using Linear Programs (LPs). To verify control invariance on a continuous domain, we leverage recently proposed Lipschitz constants of LPs to transform the problem of continuous verification into a finite number of LPs. Using concepts from differentiable optimization, we derive the safe expansion law of the control invariant set and show how it can be interpreted as a second invariance problem in the space of possible boundaries. Finally, we show how the obtained set can be used to obtain a minimally invasive safety filter in a Control Barrier Function (CBF) framework. Our work is supported by theoretical results as well as numerical examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Collision avoidance

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