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arXiv 2023年

作者： Jiang, Qi Gao, Shaohua Gao, Yao Yang, Kailun Yi, Zhonghua Shi, Hao Sun, Lei Wang, Kaiwei The State Key Laboratory of Modern Optical Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

High-quality panoramic images with a Field of View (FoV) of 360° are essential for contemporary panoramic computer vision tasks. However, conventional imaging systems come with sophisticated lens designs and heavy optical components. This disqualifies their usage in many mobile and wearable applications where thin and portable, minimalist imaging systems are desired. In this paper, we propose a Panoramic Computational Imaging Engine (PCIE) to achieve minimalist and high-quality panoramic imaging. With less than three spherical lenses, a Minimalist Panoramic Imaging Prototype (MPIP) is constructed based on the design of the Panoramic Annular Lens (PAL), but with low-quality imaging results due to aberrations and small image plane size. We propose two pipelines, i.e. Aberration Correction (AC) and Super-Resolution and Aberration Correction (SR&AC), to solve the image quality problems of MPIP, with imaging sensors of small and large pixel size, respectively. To leverage the prior information of the optical system, we propose a Point Spread Function (PSF) representation method to produce a PSF map as an additional modality. A PSF-aware Aberration-image Recovery Transformer (PART) is designed as a universal network for the two pipelines, in which the self-attention calculation and feature extraction are guided by the PSF map. We train PART on synthetic image pairs from simulation and put forward the PALHQ dataset to fill the gap of real-world high-quality PAL images for low-level vision. A comprehensive variety of experiments on synthetic and real-world benchmarks demonstrates the impressive imaging results of PCIE and the effectiveness of the PSF representation. We further deliver heuristic experimental findings for minimalist and high-quality panoramic imaging, in terms of the choices of prototype and pipeline, network architecture, training strategies, and dataset construction. Our dataset and code will be available at https://***/zju-jiangqi/PCIE-PART. Copyrig

关键词： Optical transfer function

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Salient Object Detection in Traffic Scene through the TSOD10K Dataset

arXiv

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arXiv 2025年

作者： Qiu, Yu Sun, Yuhang Mei, Jie Xiao, Lin Xu, Jing College of Information Science and Engineering Hunan Normal University Changsha410081 China National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases College of Artificial Intelligence Nankai University Tianjin300350 China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

Traffic Salient Object Detection (TSOD) aims to segment the objects critical to driving safety by combining semantic (e.g., collision risks) and visual saliency. Unlike SOD in natural scene images (NSI-SOD), which prioritizes visually distinctive regions, TSOD emphasizes the objects that demand immediate driver attention due to their semantic impact, even with low visual contrast. This dual criterion, i.e., bridging perception and contextual risk, re-defines saliency for autonomous and assisted driving systems. To address the lack of task-specific benchmarks, we collect the first large-scale TSOD dataset with pixel-wise saliency annotations, named TSOD10K. TSOD10K covers the diverse object categories in various real-world traffic scenes under various challenging weather/illumination variations (e.g., fog, snowstorms, low-contrast, and low-light). Methodologically, we propose a Mamba-based TSOD model, termed Tramba. Considering the challenge of distinguishing inconspicuous visual information from complex traffic backgrounds, Tramba introduces a novel Dual-Frequency visual State Space module equipped with shifted window partitioning and dilated scanning to enhance the perception of fine details and global structure by hierarchically decomposing high/low-frequency components. To emphasize critical regions in traffic scenes, we propose a traffic-oriented Helix 2D-Selective-Scan (Helix-SS2D) mechanism that injects driving attention priors while effectively capturing global multi-direction spatial dependencies. We establish a comprehensive benchmark by evaluating Tramba and 22 existing NSI-SOD models on TSOD10K, demonstrating Tramba’s superiority. Our research establishes the first foundation for safety-aware saliency analysis in intelligent transportation systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk perception

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Event-aided Semantic Scene Completion

arXiv

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arXiv 2025年

作者： Guo, Shangwei Shi, Hao Wang, Song Yin, Xiaoting Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Autonomous driving systems rely on robust 3D scene understanding. Recent advances in Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of RGB-based approaches, which struggle under motion blur, poor lighting, and adverse weather. Event cameras, offering high dynamic range and low latency, address these challenges by providing asynchronous data that complements RGB inputs. We present DSEC-SSC, the first real-world benchmark specifically designed for event-aided SSC, which includes a novel 4D labeling pipeline for generating dense, visibility-aware labels that adapt dynamically to object motion. Our proposed RGB-Event fusion framework, EvSSC, introduces an Event-aided Lifting Module (ELM) that effectively bridges 2D RGB-Event features to 3D space, enhancing view transformation and the robustness of 3D volume construction across SSC models. Extensive experiments on DSEC-SSC and simulated SemanticKITTI-E demonstrate that EvSSC is adaptable to both transformer-based and LSS-based SSC architectures. Notably, evaluations on SemanticKITTI-C demonstrate that EvSSC achieves consistently improved prediction accuracy across five degradation modes and both In-domain and Out-of-domain settings, achieving up to a 52.5% relative improvement in mIoU when the image sensor partially fails. Additionally, we quantitatively and qualitatively validate the superiority of EvSSC under motion blur and extreme weather conditions, where autonomous driving is challenged. The established datasets and our codebase will be made publicly at https://***/Pandapan01/EvSSC. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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An Improved SCA-BP Delay Prediction Method for Networked control Systems

An Improved SCA-BP Delay Prediction Method for Networked Con...

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Chinese Automation Congress (CAC)

作者： Jiayin Xie Yaonan Wang Haoran Tan You Wu Bowen Zhou National Engineering Research Center for Robot Visual Perception and Control College of Electrical and Information Engineering Hunan University Changsha China Hunan Retoo Intelligent Technology Co. Ltd. Changsha China

A networked control system is a system that uses communication networks to connect the various parts. However, network delay affects the performance and stability of the networked control system. In order to eliminate the impact of network delay on the control system, an improved SCA-BP delay prediction method is proposed in this paper. The proposed method optimizes the initial weights and biases of the BP neural network, and further optimizes them through backpropagation gradient descent, obtaining the final prediction model. The proposed method introduces a random perturbation factor in the sine and cosine functions, which increases the explorability and diversity of the method. And they are sorted and grouped according to the fitness values of the candidate solutions. At the same time, different update strategies are adopted based on the different groups, enabling the method to perform global and local search simultaneously. In the update strategy, a linear function is also introduced in addition to the sine and cosine functions, enabling the method to maintain the direction of the current optimal solution to a certain extent. Finally, the superiority and effectiveness of the method compared with traditional methods are verified through some comparative simulations, indicating that the method has a strong delay prediction ability.

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Skeleton-Based Human Action Recognition with Noisy Labels

Skeleton-Based Human Action Recognition with Noisy Labels

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Yi Xu Kunyu Peng Di Wen Ruiping Liu Junwei Zheng Yufan Chen Jiaming Zhang Alina Roitberg Kailun Yang Rainer Stiefelhagen Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Institute for Artificial Intelligence University of Stuttgart Germany School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Understanding human actions from body poses is critical for assistive robots sharing space with humans in order to make informed and safe decisions about the next interaction. However, precise temporal localization and annotation of activity sequences is time-consuming and the resulting labels are often noisy. If not effectively addressed, label noise negatively affects the model’s training, resulting in lower recognition quality. Despite its importance, addressing label noise for skeleton-based action recognition has been overlooked so far. In this study, we bridge this gap by implementing a framework that augments well-established skeleton-based human action recognition methods with label-denoising strategies from various research areas to serve as the initial benchmark. Observations reveal that these baselines yield only marginal performance when dealing with sparse skeleton data. Consequently, we introduce a novel methodology, NoiseEraSAR, which integrates global sample selection, co-teaching, and Cross-Modal Mixture-of-Experts (CM-MOE) strategies, aimed at mitigating the adverse impacts of label noise. Our proposed approach demonstrates better performance on the established benchmark, setting new state-of-the-art standards. The source code for this study will be made accessible at https://***/xuyizdby/NoiseEraSAR.

关键词： Training Source coding Noise Training data Benchmark testing Skeleton Noise measurement Human activity recognition Streams Standards

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Tightly-Coupled LiDAR-visual SLAM Based on Geometric Features for Mobile Agents

Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Feature...

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IEEE International Conference on robotics and Biomimetics

作者： Ke Cao Ruiping Liu Ze Wang Kunyu Peng Jiaming Zhang Junwei Zheng Zhifeng Teng Kailun Yang Rainer Stiefelhagen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard to develop a dedicated algorithm for mobile robots due to dynamic and challenging situations, such as poor lighting conditions and motion blur. To tackle this issue, we propose a tightly-coupled LiDAR-visual SLAM based on geometric features, which includes two sub-systems (LiDAR and monocular visual SLAM) and a fusion framework. The fusion framework associates the depth and semantics of the multi-modal geometric features to complement the visual line landmarks and to add direction optimization in Bundle Adjustment (BA). This further constrains visual odometry. On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features. It adjusts the direction of linear feature points and filters out outliers, leading to a higher accurate odometry system. Finally, we employ a module to detect the subsystem’s operation, providing the LiDAR subsystem’s output as a complementary trajectory to our system while visual subsystem tracking fails. The evaluation results on the public dataset M2DGR, gathered from ground robots across various indoor and outdoor scenarios, show that our system achieves more accurate and robust pose estimation compared to current state-of-the-art multi-modal methods.

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Humanoid robot Technology and Industry Development

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Strategic Study of CAE 2025年第1期27卷 150-167页

作者： Chenghao, Xu Yaonan, Wang Yang, Mo Qing, Zhu College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China

With the continuous breakthroughs in technologies such as artificial intelligence, advanced manufacturing, and new materials, China’s humanoid robot industry is experiencing explosive growth. Technological innovation and supportive policies have fostered a diversified and competitive landscape. However, China’s humanoid robot industry still faces significant challenges, including lagging core technologies, high difficulty in mass production, and obstacles to commercialization. This study explores the major subfields and technological frontiers of humanoid robotics, offering an in-depth analysis of global trends in policies, technologies, and industrial development. It examines the current state of humanoid robotics in China and identifies key opportunities and challenges. Furthermore, the study proposes strategic recommendations to address these challenges, focusing on technological innovation, pilot demonstration, improvement in laws and regulations, and policy support. Specifically, the research recommends encouraging breakthroughs in core technologies, strengthening industrial layout, building humanoid robot infrastructure, and implementing demonstrative projects. These efforts aim to help China’s humanoid robot industry overcome technical challenges, improve its industrial ecosystem, and achieve large-scale production and commercialization, thereby enhancing the global competitiveness of the industry. © 2025 Beijing Zhongguo Gongcheng Kexue Zazhishe. All rights reserved.

关键词： Anthropomorphic robots

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Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

arXiv

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arXiv 2024年

作者： Liu, Ruiping Zhang, Jiaming Peng, Kunyu Chen, Yufan Cao, Ke Zheng, Junwei Sarfraz, M. Saquib Yang, Kailun Stiefelhagen, Rainer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Mercedes-Benz Tech Innovation Germany School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework. However, the modality incompleteness in multi-modal segmentation remains under-explored. In this work, we establish a task called Modality-Incomplete Scene Segmentation (MISS), which encompasses both system-level modality absence and sensor-level modality errors. To avoid the predominant modality reliance in multi-modal fusion, we introduce a Missing-aware Modal Switch (MMS) strategy to proactively manage missing modalities during training. Utilizing bit-level batch-wise sampling enhances the model’s performance in both complete and incomplete testing scenarios. Furthermore, we introduce the Fourier Prompt Tuning (FPT) method to incorporate representative spectral information into a limited number of learnable prompts that maintain robustness against all MISS scenarios. Akin to fine-tuning effects but with fewer tunable parameters (1.1%). Extensive experiments prove the efficacy of our proposed approach, showcasing an improvement of 5.84% mIoU over the prior state-of-the-art parameter-efficient methods in modality missing. The source code is publicly available at https://***/RuipingL/MISS. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensory perception

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A Muti-stage Selection Filter Based on Wavelet Packet and2DCNN for Fault Diagnosis of Rotating Machinery

A Muti-stage Selection Filter Based on Wavelet Packet and2DC...

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第42届中国控制会议

作者： Wenbin He Jianxu Mao Li Liu Zhe Li Miao Yang Yaonan Wang College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University State Grid Hunan Electric Power Company Limited Research Institute

Fault information of rotating machinery is often drowned in strong noise signals, so it is crucial to accurately identify faults from high-intensity noise signals. In this article, an end-to-end fault diagnosis model is developed, which consists of a multi-stage selection filter based on wavelet packet and 2D-CNN. First, the original measured mechanical signals were processed by the three-level wavelet packet decomposition to obtain eight sub-bands with coefficient matrices. Second, the signal is reconstructed using different numbers of sub-bands, where the number is increased by one at a time to obtain eight different multi-stage reconstructed signals. Third, the reconstructed signals are reorganized into 2D signal maps;and a parallel training network is constructed using signal maps and 2D-CNN to achieve fault classification. Then, guided by the training results, eight parallel classification results are compared, so as to train the best fault diagnosis model. Finally, the simulation experiment based on a bearing data set illustrates the proposed multi-stage selection filter is effective and feasible in application.

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Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

Fourier Prompt Tuning for Modality-Incomplete Scene Segmenta...

引用

IEEE Symposium on Intelligent Vehicle

作者： Ruiping Liu Jiaming Zhang Kunyu Peng Yufan Chen Ke Cao Junwei Zheng M. Saquib Sarfraz Kailun Yang Rainer Stiefelhagen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Mercedes-Benz Tech Innovation Germany School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

关键词： Training Rain Source coding Semantic segmentation Semantics Switches Benchmark testing

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