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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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Leveraging Spatial Attention and Edge Context for Optimized Feature Selection in visual localization

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第2期23卷 418-428页

作者： Istighfarin, Nanda Febri Jo, Hyunggi Jeonbuk Natl Univ Div Elect Engn Jeonju 54896 South Korea

visual localization determines an agent's precise position and orientation within an environment using visual data. It has become a critical task in the field of robotics, particularly in applications such as autonomous navigation. This is due to the ability to determine an agent's pose using cost-effective sensors such as RGB cameras. Recent methods in visual localization employ scene coordinate regression to determine the agent's pose. However, these methods face challenges as they attempt to regress 2D-3D correspondences across the entire image region, despite not all regions providing useful information. To address this issue, we introduce an attention network that selectively targets informative regions of the image. Using this network, we identify the highest-scoring features to improve the feature selection process and combine the result with edge detection. This integration ensures that the features chosen for the training buffer are located within robust regions, thereby improving 2D-3D correspondence and overall localization performance. Our approach was tested on the outdoor benchmark dataset, demonstrating superior results compared to previous methods.

关键词： Attention network computer vision edge detector scene coordinate regression visual localization

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iLoc: An Adaptive, Efficient, and Robust visual localization System

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 2709-2726页

作者： Yin, Peng Zhao, Shiqi Wang, Jing Ge, Ruohai Ji, Jianmin Hu, Yeping Liu, Huaping Han, Jianda City Univ Hong Kong Hong Kong Peoples R China Univ Southern Calif Los Angeles CA 90089 USA Univ Sci & Technol China Hefei 230000 Peoples R China Lawrence Livermore Natl Lab Livermore CA 94550 USA Tsinghua Univ Beijing 100084 Peoples R China Nankai Univ Tianjin 300071 Peoples R China

In this article, we introduce iLoc, an innovative visual localization system designed to enhance the autonomy and adaptability of robotic agents in long-term and large-scale applications. iLoc specializes in: 1) extracting stable and consistent descriptors for place recognition, unaffected by changes in viewpoint and illumination;2) performing swift and precise global relocalization to establish a robot's position within a large and complex environment;and 3) generating real-time tracking trajectories aligned with reference maps, ensuring continual orientation within known spaces. Distinctively, iLoc incorporates a transformer-based learning module and an attention-enhanced recognition approach, enabling it to adapt to diverse environmental and viewpoint conditions. iLoc leverages a coarse-to-fine global feature matching technique for enhanced localization and integrates robust state estimation combining visual odometry and loop closures through local refinement and pose graph optimization. iLoc demonstrates remarkable proficiency in place recognition, achieving localization over distances of up to 2 km within 0.5 s with average accuracy at 1 m. It maintains stable localization accuracy, even under variable conditions. Its versatile design allows integration across various environments, significantly broadening the scope of universal localization capabilities in robotics. iLoc represents a substantial step forward in visual-based localization systems, delivering unparalleled speed and accuracy in place recognition. Its ability to adapt and respond to diverse environmental stimuli marks it as a crucial tool in advancing the field of robotic localization.

关键词： Location awareness visualization Robots Accuracy Feature extraction Adaptive systems Transformers Image recognition Urban areas Real-time systems Domain-invariant place recognition optimization visual localization

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3D Model-Free visual localization System From Essential Matrix Under Local Planar Motion

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 2090-2107页

作者： Jiao, Yanmei Zhang, Binxin Jiang, Peng Wang, Chaoqun Lu, Haojian Xiong, Rong Wang, Yue Hangzhou Normal Univ Sch Informat Sci & Engn Hangzhou 311121 Peoples R China Shandong Univ Sch Control Sci & Engn Shandong 250100 Peoples R China Zhejiang Univ State Key Lab Ind Control & Technol Hangzhou 310058 Peoples R China

visual localization plays a critical role in the functionality of low-cost autonomous mobile robots. Contemporary leading methods for precise visual localization are predominantly 3D scene-specific, necessitating extra computational and memory overhead to construct a 3D scene model in novel environments. An alternative approach of directly using a database of 2D images for visual localization offers more flexibility. However, such methods currently suffer from limited localization accuracy. In this paper, we propose an accurate and robust multiple checking-based 3D model-free visual localization system to address the aforementioned issues. To ensure high accuracy, our focus is on estimating the pose of a query image relative to the retrieved database images using 2D-2D feature matches. Theoretically, by incorporating the local planar motion constraint into both the estimation of the essential matrix and the triangulation stages, we reduce the minimum required feature matches for absolute pose estimation, thereby enhancing the robustness of outlier rejection. Additionally, we introduce a multiple-checking mechanism to ensure the correctness of the solution throughout the solving process. The efficacy of our approach is substantiated through both qualitative and quantitative assessments on simulated and two real-world datasets evidencing significant improvements in accuracy and robustness provided by our 3D model-free visual localization system. Note to Practitioners-The motivation of this article stems from the need to develop an accurate visual localization system with simplicity and flexibility of map construction and easy adaption to new environments. Such a system holds great practical value for a range of applications, including warehouse robots, service robots, and countless others. Existing visual localization systems that achieve high accuracy are dependent on a pre-built accurate 3D scene map, which pose challenges in terms of map construction and consume si

关键词： Minimal solutions essential matrix local planar motion visual localization

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Lunar visual localization Method Based on Crater Geohash Encoding and Consistency Matching

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REMOTE SENSING 2025年第9期17卷 1493-1493页

作者： Li, Siyuan He, Yuntao Huang, Jianbin Li, Tao Wang, Anran Zhang, Shuo Ren, Jiaqiong Wu, Jiaxuan Beihang Univ Sch Elect Informat Engn Beijing 100191 Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beijing Inst Control Engn Beijing 100190 Peoples R China

Accurate and robust visual localization is essential for autonomous lunar landing. This study presents a new crater-based method that addresses challenges posed by environmental uncertainties such as camera pose deviations, the number of craters within the scene, and the image brightness. Our method combines crater Geohash encoding for efficient database retrieval with an improved principal component analysis (PCA) for crater detection. The detected craters are ranked, retaining those with fewer but more accurate detections to meet localization requirements. Crucially, we introduce a consistency matching technique that exploits the linear relationship between position shifts and pixel offsets, enhancing both localization accuracy and computational efficiency. Experimental results across diverse scenes and simulation conditions demonstrate 100% matching accuracy with an average matching time under 0.8 s. Reprojection errors remain below 3 px, significantly outperforming methods like triangle similarity matching (TSM) and direct matching (DM). This validates the proposed method's high precision and stability for near real-time lunar localization.

关键词： Geohash encoding pixel offsets reprojection visual localization

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