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visual localization of robotic end effector via fusion of 3D Gaussian Splatting and heuristic optimization algorithm

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MEASUREMENT 2025年 242卷

作者： Hu, Qichun Wei, Xiaolong Cheng, Ronghui Xu, Haojun Cai, Yu Yin, Yizhen He, Weifeng AF Engn Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian 710038 Shaanxi Peoples R China Shenyang Aeroengine Res Inst AECC Shenyang 110015 Liaoning Peoples R China Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China

The visual localization of the robotic end effector holds significant importance and value in intelligent manufacturing. Regarding this issue, a pose estimation framework integrating the radiance field rendering and an improved differential evolution algorithm is proposed in this paper. Firstly, multi-view images of the object are collected by an industrial camera carried on the robotic arm. Then, a 3D Gaussian Splatting model is trained, and the observation trajectory of the virtual camera is designed based on the query image in Nerfstudio for visualization. The key frames and the corresponding pose matrices of the rendered videos along the trajectory are extracted. Subsequently, the improved differential evolution algorithm through double-layer optimization proposed in this paper is employed to conduct similarity matching between the query image and the extracted rendered images. The pose matrix corresponding to the most similar rendered image is the estimated pose of the query image. Through experiments, this paper explores the influence of the frame sampling rate on the pose estimation accuracy, and conducts comparative and ablation experiments with the commonly used methods at present. The experimental results demonstrate that the proposed method possesses good feasibility and outstanding performance.

关键词： visual localization Pose estimation 3D gaussian splatting Differential evolution Neural radiance field

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visual localization and Topographic Mapping for Zhurong Rover in Tianwen-1 Mars Mission

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6393-6408页

作者： Wan, Wenhui Wang, Jia Liu, Zhaoqin Di, Kaichang Peng, Man Wang, Yexin Liu, Bin Hu, Guolin Han, Shaojin Li, Xin Yu, Tianyi Li, Lichun Liu, Chuankai Chinese Acad Sci Aerosp Informat Res Inst Key Lab Remote Sensing & Digital Earth Beijing 100101 Peoples R China Beijing Aerosp Control Ctr Beijing 100094 Peoples R China

This article presents the methods, results, and applications of visual localization and topographic mapping for the Zhurong rover in China's Tianwen-1 Mars mission. New techniques, namely vignette correction and online camera mast calibration, were developed for the images captured by the rover's navigation and terrain cameras (NaTeCam) to enhance the mapping qualities. During surface operations, photogrammetric techniques were routinely applied at the traveling waypoints. The NaTeCam images were obtained to generate 3-D topographic mapping products at the centimeter level to support obstacle analysis and rover path planning. Cross-site visual localization at adjacent waypoints was conducted to reduce the accumulated position errors in the onboard dead-reckoning result. visual localization refinement was also employed by matching the digital orthophoto map (DOM) from the rover images and the orbital basemap at waypoints where distinct terrain features were available to further eliminate the localization errors. Compared with the reference rover traverse extracted from a high-resolution imaging science experimental image in which the actual rover track is visible, the accuracies of the rover traverses generated from cross-site visual localization and DOM matching refinement are estimated to be 1.75% and 0.50% of the traverse length. The actual total traverse length is 2009 m, which is 88 m longer than 1921 m from the odometer reading, revealing a forward wheel slipping caused by the downslope trend of the southward traverse. The visual localization and topographic mapping results of the Zhurong rover have greatly supported the surface exploration planning, object approaching, and scientific investigations in the mission.

关键词： Location awareness Surface topography visualization Mars Interplanetary exploration Cameras Surface morphology Planetary orbits Space vehicles Path planning Mars mission topographic mapping online camera mast calibration vignette correction visual localization Zhurong rover

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visual localization Domain for Accurate V-SLAM from Stereo Cameras

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SENSORS 2025年第3期25卷 739-739页

作者： Di Salvo, Eleonora Bellucci, Sara Celidonio, Valeria Rossini, Ilaria Colonnese, Stefania Cattai, Tiziana Sapienza Univ Rome Dept Informat Engn Elect & Telecommun I-00184 Rome Italy

Trajectory estimation from stereo image sequences remains a fundamental challenge in visual Simultaneous localization and Mapping (V-SLAM). To address this, we propose a novel approach that focuses on the identification and matching of keypoints within a transformed domain that emphasizes visually significant features. Specifically, we propose to perform V-SLAM in a visual localization Domain (VILD), i.e., a domain where visually relevant feature are suitably represented for analysis and tracking. This transformed domain adheres to information-theoretic principles, enabling a maximum likelihood estimation of rotation, translation, and scaling parameters by minimizing the distance between the coefficients of the observed image and those of a reference template. The transformed coefficients are obtained from the output of specialized Circular Harmonic Function (CHF) filters of varying orders. Leveraging this property, we employ a first-order approximation of the image-series representation, directly computing the first-order coefficients through the application of first-order CHF filters. The proposed VILD provides a theoretically grounded and visually relevant representation of the image. We utilize VILD for point matching and tracking across the stereo video sequence. The experimental results on real-world video datasets demonstrate that integrating visually-driven filtering significantly improves trajectory estimation accuracy compared to traditional tracking performed in the spatial domain.

关键词： visual localization circular harmonic functions visually relevant features stereo camera

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Descriptor-based optical flow quality assessment and error model construction for visual localization

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2025年 221卷 143-154页

作者： Lei, Jietao Liu, Jingbin Zhang, Wei Li, Mengxiang Hyyppa, Juha Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Wuhan Geodetect Technol Co Ltd Wuhan 430022 Peoples R China Shenzhen R&D Ctr State Key Lab Informat Engn Surve Shenzhen 518057 Peoples R China Finnish Geospatial Res Inst Dept Remote Sensing & Photogrammetry Natl Land Survey Finland Vuorimiehentie 5 Espoo 02150 Finland

Precise matching of visual features between frames is crucial for the robustness and accuracy of visual odometry and SLAM (Simultaneous localization and Mapping) systems. However, factors such as complex illumination and texture variations may cause significant errors in feature correspondences that will degrade the accuracy of visual localization. In this paper, we utilize the feature descriptor to validate and assess the correspondence quality of the optical flow algorithm, and establish the information matrix of visual measurements, which is used for improving the accuracy of visual localization in the nonlinear optimization framework. This proposed approach of optical flow quality assessment leverages the complementary advantages of the optical flow algorithm and descriptor matching, and it is applicable to other visual odometry or SLAM systems that use the optical flow algorithm for feature correspondence. We first demonstrate through simulation experiments the statistical correlation between optical flow error and descriptor Hamming distance. Subsequently, based on the statistical correlation, the optical flow tracking error is quantitatively estimated using the descriptor Hamming distance. Asa result, features with large tracking errors are rejected as outliers, and other features are remained with an adequate error model, i.e. information matrix in the nonlinear optimization, which corresponds with the visual tracking error. Furthermore, rather than direct tracking error between the initial observation frame and the current frame, we proposed the cumulative tracking error for successive frames (CTE-SF) to improve the efficiency of descriptor extraction in successive visual tracking, as it requires no the construction of multi- scale image pyramids. We evaluated the proposed solution using the open datasets and our developed in-house embedded positioning device. The results indicate that the proposed solution can improve the accuracy of visual odometry system

关键词： Indoor positioning Ubiquitous positioning visual localization Optical flow ORB descriptor Error model

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MFICNet: A Multimodality Fusion Network With Information Compensation for Accurate Indoor visual localization

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Fan, Zhendong Dai, Kun Guo, Jilong Jiang, Zhiqiang Gao, Hongbo Xie, Tao Li, Ruifeng Wang, Ke Harbin Inst Technol HIT State Key Lab Robot & Syst Harbin 150006 Peoples R China Harbin Engn Univ Yantai Res Inst Yantai 150001 Peoples R China

As a crucial technology in numerous visual applications, visual localization has been extensively studied, with an effective solution known as scene coordinate regression (SCoRe). Generally, SCoRe methods generate scene coordinates using convolutional neural networks (CNNs) and then determine the camera pose with a PnP algorithm. While these methods demonstrate impressive localization accuracy, they primarily rely on a single modality, e.g., RGB camera, which leads to texture dependency and structural ambiguity problems. Specifically, perceptual confusion caused by similar image textures in real indoor scenes causes a severe decline in localization accuracy, as the performance of the networks heavily depends on the semantic information of objects. In addition, current methods struggle to robustly recover the structural details of objects because RGB images lack 3-D geometric structural information. We think that these two issues stem from the inherent limitations of single modality. There is potential for complementarity between semantic and structural information. Toward this end, we propose MFICNet, a novel visual localization network that simultaneously utilizes RGB and depth images to achieve accurate visual localization. This pioneering architecture establishes a new paradigm for multimodality-based visual localization. Technically, MFICNet employs a heterogeneous backbone to extract features from RGB images and depth images separately. The structural feature obtained from depth images enhances the identifiability of similar image patches and imposes structural constraints for scene coordinates. After that, an information compensation module is introduced to evaluate the contributions of semantic and structural features and perform deep fusion to generate discriminative features. Extensive experiments are conducted on the 7-Scenes dataset and our newly released indoor dataset STIVL, which specializes in similar textures. The results show that MFICNet significan

关键词： Location awareness Cameras visualization Feature extraction Semantics Accuracy Three-dimensional displays Databases Prediction algorithms Point cloud compression Information compensation scene coordinate regression (SCoRe) visual localization

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SplatLoc: 3D Gaussian Splatting-based visual localization for Augmented Reality

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2025年第5期31卷 3591-3601页

作者： Zhai, Hongjia Zhang, Xiyu Zhao, Boming Li, Hai He, Yijia Cui, Zhaopeng Bao, Hujun Zhang, Guofeng Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China RayNeo Shenzhen Peoples R China

visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches. Code and data are available at project page: https://***/splatloc.

关键词： visual localization 3D Gaussian Splatting Augmented Reality Augmented Reality Augmented Reality

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HSCNet plus plus : Hierarchical Scene Coordinate Classification and Regression for visual localization with Transformer

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2024年第7期132卷 2530-2550页

作者： Wang, Shuzhe Laskar, Zakaria Melekhov, Iaroslav Li, Xiaotian Zhao, Yi Tolias, Giorgos Kannala, Juho Aalto Univ Espoo Finland Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague 6 Czech Republic

visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The proposed method, which is an extension of HSCNet, allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image localization on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and the combined indoor scenes.

关键词： Scene coordinate regression Hierarchical classification visual localization Transformers

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Spatial Gating with Hybrid Receptive Field for Robot visual localization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-13页

作者： Zhou, Shuhong Wu, Junjun Lu, Qinghua Foshan Univ Sch Mechatron Engn & Automat Foshan 528225 Guangdong Peoples R China

visual localization for mobile robots is a sophisticated and challenging task that necessitates the extraction of pertinent scene information from images obtained by the robot's visual sensors to ascertain its position within an environment. The task is complicated by variations in environmental factors which affect the accuracy of localization. To address the challenges of visual localization on variations of illumination, seasons, and viewpoints, this paper proposes a visual localization network based on a gated selection and hybrid receptive field. We utilize a fine-tuned DINOv2 for local feature extraction and leverage a hybrid receptive field to enhance the diversity of visual features. Furthermore, our approach employs spatial gating to dynamically and effectively select and aggregate the advantageous spatial features. Extensive experiments demonstrate that the visual localization performance of our approach surpasses existing methods on multiple challenging datasets, particularly achieving a Recall@1 metric of 69.2% on the NordLand dataset, which signifies a 10.8% enhancement compared to MixVPR.

关键词： Mobile robot visual localization Gating network Global descriptor Image retrieval

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Multimodal Absolute visual localization for Unmanned Aerial Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第11期73卷 16402-16415页

作者： Liu, Zhunga Li, Huandong Zhang, Zuowei Lyu, Yanyi Xiong, Jiexuan Northwestern Polytech Univ Sch Automat Key Lab Informat Fus Technol Minist Educ Xian 710072 Peoples R China

Absolute visual localization methods with visible sensors have been widely used for Unmanned Aerial Vehicles (UAVs) in GPS-denied environments. However, the visible real-time images could be easily influenced by illumination in applications, and it makes the localization system unable to work. In this paper, an image registration network (NIVnet) is proposed to deal with near-infrared real-time and visible reference images for the multimodal visual localization system of UAVs in GPS-denied environments. In NIVnet, a new feature extraction strategy is first developed to reduce the modality differences of input images. The input images are embedded into a common feature space with disentangled representations. Then, a new bidirectional matching layer is proposed by matching a pair of input images twice in one registration process. Such matching layer can effectively handle large geometric deformations between the images to be registered. Finally, an intensity loss is introduced to further enhance performance by measuring the similarity of monomodal images rather than multimodal images. The proposed NIVnet can predict the affine transformation parameters in an end-to-end way, and thus the localization of UAVs is accelerated. Extensive experiments on three synthetic datasets are conducted to demonstrate the validity of NIVnet, and experimental results show that NIVnet can effectively improve localization accuracy.

关键词： Location awareness visualization Feature extraction Image registration Real-time systems Autonomous aerial vehicles Task analysis UAV localization visual localization image registration cross-modality deep learning

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Exploring Deep Learning-Based visual localization Techniques for UAVs in GPS-Denied Environments

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IEEE ACCESS 2024年 12卷 113049-113071页

作者： Al-Jarrah, Omar Y. Shatnawi, Ahmed S. Shurman, Mohammad M. Ramadan, Omar A. Muhaidat, Sami Jordan Univ Sci & Technol Fac Comp & Informat Technol Dept Network Engn & Secur Irbid 22110 Jordan Jordan Univ Sci & Technol Fac Engn Dept Mech Engn Irbid 22110 Jordan Khalifa Univ KU Res Ctr 6G Dept Comp & Informat Engn Abu Dhabi U Arab Emirates

Unmanned Aerial Vehicles (UAVs) have proliferated across diverse domains. However, optimal UAV operations necessitate precise and reliable navigation systems. UAVs predominantly rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS), for navigation. Nevertheless, GNSS signals are susceptible to blockage, reflection, and spoofing, introducing significant risks, including navigation loss and potential UAV loss. This research investigates cutting-edge navigation solutions, emphasizing deep learning-based visual localization approaches tailored for UAVs. Our focus is on scenarios characterized by GPS-denied environments where GPS signals may be absent or unreliable. We provide a comprehensive review of contemporary deep learning-based visual localization approaches and compare them to traditional aerial visual localization methods, such as template matching and feature matching. This comparison highlights both the potential benefits and challenges associated with these approaches. Furthermore, we systematically evaluate and classify recent deep learning-based methods based on main criteria, including model type/architecture, reference imagery, operational context, and resultant accuracy levels. Our findings underscore the substantial promise inherent in various approaches while also shedding light on their unique deployment challenges. Finally, we discuss potential research directions, to inspire further innovations and progress in this domain. The ultimate goal is to develop more accurate, dependable, and secure navigation solutions for UAVs.

关键词： Location awareness visualization Accuracy Reviews Global navigation satellite system Navigation Autonomous aerial vehicles Deep learning GPS-denied localization visual localization navigation UAVs

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