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A tracker pose optimization method for robotic measuring system based on spatial distance constraints

MEASUREMENT 2025年 243卷

作者： Lin, Xiaoyu Wang, Ziwei Yang, Yifan Qi, Bosong Zhang, Xiaojian Yan, Sijie Ding, Han Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Luo Yu Rd 1037 Wuhan 430074 Peoples R China Hust Wuxi Res Inst Yan Xin Rd 329 Wuxi 214174 Peoples R China

In large-scale metrology (LSM), the transformation of the tracker base frame (TBF) is a predominant method to enlarge the field of view (FOV) of the tracking sensor for full-field 3D measurements. Nevertheless, such a process will introduce cumulative errors and significantly diminish the global point cloud alignment accuracy. To address this problem, we propose a novel tracker pose optimization method for TBF transformation. A pose graph optimization (PGO) model based on spatial distance constraints is implemented to improve the tracker pose accuracy. We also adopt a robust coefficient and a damping factor to simplify the experimental process and stabilize the convergence results. Simulations and experiments on high-speed train surfaces are conducted to validate our method's accuracy and effectiveness. The results indicate that our optimization method outperforms two existing methods in spatial positioning accuracy and point cloud alignment accuracy, which showcases its practical applicability and superiority in manufacturing scenarios.

关键词： Tracker base frame transformation Photogrammetry tracking pose graph optimization Spatial distance constraints Large-scale metrology Full-field 3D measurement

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IPAL: Infinite Planes as Lines for Consistent Mapping in Indoor Multifloor Environments

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Jin, Xiaofeng Ge, Jianfei Xiao, Jiangjian Bu, Ningbo Xu, Gen Zhejiang Univ Technol Dept Mech Engn Hangzhou 310023 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn Ningbo 315201 Peoples R China

Indoor high-precision maps are harder to obtain. Numerous researchers considered the extendibility of indoor plane features to constrain the poses and improve mapping consistency. However, the usage of parametric planar or line features requires complex modeling, and threshold-based matching methods are eager to lead to degradation. Indeed, planes can be more simply represented as line projections, with the projection direction perpendicular to the plane's normal vector. In this article, we proposed a novel mapping optimization framework that incorporates building outline features specifically designed for multistory buildings. The framework consists of three main components: LiDAR bundle adjustment (LBA), global constraints based on building outline features, and factor graph optimization. The method extracts the ground only for horizontal correction, while the core idea is based on the assumption of vertical structure, projecting the point cloud contours of multistory buildings to the top view perspective for independent spatial pose correlation, which is a strong constraint and very effective. Finally, constraints are constructed based on keyframes and intermediate frames are refined by a factor graph. Through extensive experiments conducted on multistory buildings, we demonstrate that our algorithm significantly enhances mapping consistency and local accuracy compared to existing methods.

关键词： Global consistency LiDAR bundle adjustment (LBA) pose graph optimization

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Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2024年 103卷

作者： Ott, Felix Heublein, Lucas Ruegamer, David Bischl, Bernd Mutschler, Christopher Fraunhofer IIS Fraunhofer Inst Integrated Circuits IIS Nordostpk 84 D-90411 Nurnberg Germany Ludwig Maximilians Univ Munchen Ludwigstr 33 D-80539 Munich Germany Tech Univ Dortmund August Schmidt Str 1 D-44227 Dortmund Germany Munich Ctr Machine Learning MCML Ludwigstr 33 D-80539 Munich Germany

The localization of objects is essential in many applications, such as robotics, virtual and augmented reality, and warehouse logistics. Recent advancements in deep learning have enabled localization using monocular cameras. Traditionally, structure from motion (SfM) techniques predict an object's absolute position from point cloud, while absolute pose regression (APR) methods use neural networks to understand the environment semantically. However, both approaches face challenges from environmental factors like motion blur, lighting changes, repetitive patterns, and featureless areas. This study addresses these challenges by incorporating additional information and refining absolute pose estimates with relative pose regression (RPR) methods. RPR also struggles with issues like motion blur. To overcome this, we compute the optical flow between consecutive images using the Lucas-Kanade algorithm and use a small recurrent convolutional network predict relative poses. Combining absolute and relative poses is difficult due to differences between global and local coordinate systems. Current methods use pose graph optimization (PGO) to align these poses. this work, we propose recurrent fusion networks to better integrate absolute and relative pose predictions, enhancing the accuracy of absolute pose estimates. We evaluate eight different recurrent units and create simulation environment to pre-train the APR and RPR networks for improved generalization. Additionally, record a large dataset of various scenarios in a challenging indoor environment resembling a warehouse with transportation robots. Through hyperparameter searches and experiments, we demonstrate that our recurrent fusion method outperforms PGO in effectiveness.

关键词： Visual self-localization Structure from motion pose regression Optical flow pose graph optimization Recurrent pose fusion Synthetic transfer learning Challenging environment

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Multi-View Registration of Partially Overlapping Point Clouds for Robotic Manipulation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第10期9卷 8451-8458页

作者： Xie, Yuzhen Song, Aiguo Southeast Univ Sch Instrument Sci & Engn State Key Lab Digital Med Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China

Point cloud registration is a fundamental task in intelligent robots, aiming to achieve globally consistent geometric structures and providing data support for robotic manipulation. Due to the limited view of measurement devices, it is necessary to collect point clouds from multiple views to construct a complete model. Previous multi-view registration methods rely on sufficient overlap and registering all pairs of point clouds, resulting in slow convergence and high cumulative errors. To solve these challenges, we present a multi-view registration method based on the point-to-plane model and pose graph. We introduce a robust kernel into the objective function to diminish registration errors caused by mismatched points. Additionally, an enhanced Euclidean clustering method is proposed for extracting object point clouds. Subsequently, by establishing pose constraints on non-adjacent frames of point clouds, the cumulative error is reduced, achieving global optimization based on the pose graph. Experimental results demonstrate the robustness of our method with respect to overlap ratios, successfully registering point clouds with overlap ratio exceeding 30$%. In comparison to other techniques, our method can reduce the E (R) of multi-view registration by 13.54$% and E (t) by 18.72$%, effectively reducing the cumulative error.

关键词： Point cloud registration multi-view registration robust kernel function pose graph optimization Point cloud registration multi-view registration robust kernel function pose graph optimization

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MoLO: Drift-free lidar odometry using a 3D model

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PHOTOGRAMMETRIC RECORD 2024年第187期39卷 641-663页

作者： Zhao, H. Zhao, Y. Tomko, M. Khoshelham, K. Univ Melbourne Dept Infrastructure Engn 6th FloorMelbourne Connect 700 Swanston St Carlton Vic 3053 Australia

LiDAR odometry enables localising vehicles and robots in the environments where global navigation satellite systems (GNSS) are not available. An inherent limitation of LiDAR odometry is the accumulation of local motion estimation errors. Current approaches heavily rely on loop closure to optimise the estimated sensor poses and to eliminate the drift of the estimated trajectory. Consequently, these systems cannot perform real-time localization and are therefore not practical for a navigation task. This paper presents MoLO, a novel model-based LiDAR odometry approach to achieve real-time and drift-free localization using a 3D model of the environment containing planar surfaces, namely the structural elements of buildings. The proposed approach uses a 3D model of the environment to initialise the LiDAR pose and includes a scan-to-scan registration to estimate the pose for consecutive LiDAR scans. Re-registering LiDAR scans to the 3D model at a certain frequency provides the global sensor pose and eliminates the drift of the trajectory. pose graphs are built frequently to acquire a smooth and accurate trajectory. A geometry-based method and a learning-based method to register LiDAR scans with the 3D model are tested and compared. Experimental results show that MoLO can eliminate drift and achieve real-time localization while providing an accuracy equivalent to loop closure optimization. Each LiDAR scan is registered with its previous scan to estimate the transformation. Periodically, a LiDAR scan will be registered with the 3D model and pose graph optimization is then used to optimise the pose of each LiDAR scan between the two scans registered with the 3D ***

关键词： drift elimination localization pose graph optimization registration sensor pose

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SCE-LIO: An Enhanced LiDAR Inertial Odometry by Constructing Submap Constraints

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 10295-10302页

作者： Sun, Chao Huang, Zhishuai Wang, Bo Xiao, Mancheng Leng, Jianghao Li, Jiajun Beijing Inst Technol Shenzhen Automot Res Inst Shenzhen 518118 Peoples R China Beijing Inst Technol Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China ShenZhen Boundless Sensor Technol CompanyLtd Shenzhen 518118 Peoples R China

In LiDAR-based Simultaneous Localization and Mapping (SLAM) systems, loop closure detection is crucial for enhancing the accuracy of odometry. However, constraints from loop closure detection are only provided when a loop is detected and can only enhance odometry accuracy at specific moments. Therefore, this letter proposes a LiDAR inertial odometry system that periodically provides submap constraints to the pose graph and enhances odometry accuracy through pose graph optimization. The system represents LiDAR keyframes as a collection of submaps containing overlapping information during the process of creating submap constraints. The optimal pose transformations between submaps, determined using the Iterative Closest Point (ICP) algorithm with point-to-line and point-to-plane methods, are recognized as submap constraints. During the backend optimization phase, submap constraints and adjacent LiDAR keyframe constraints are integrated into the pose graph. The pose graph is then optimized using the pose graph optimization method to achieve the optimal LiDAR pose estimation. Additionally, To further enhance pose estimation, point-to-plane correspondence is established by considering the differences in normal vectors of feature points between the scan and the map, a feature extraction method based on ground segmentation is proposed, and an integrated initial positioning module is created by incorporating preintegration and scan-to-scan matching. The results of simulation, public datasets and vehicle experiments show that the accuracy of the proposed algorithm is significantly improved compared to the advanced SLAM algorithm.

关键词： Laser radar Odometry Feature extraction Accuracy Simultaneous localization and mapping Vectors Point cloud compression Robustness pose estimation optimization methods ICP pose graph optimization SLAM submap

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LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2024年 5卷 296-306页

作者： Yu, Man Gong, Keyang Zhao, Weihua Liu, Rui Changan Univ Sch Construct Machinery Xian 710064 Peoples R China Geely Automot Res Inst Ningbo Co Intelligent Driving Ctr Ningbo 315336 Peoples R China Xian Aeronaut Inst Sch Vehicle Engn Xian 710061 Peoples R China Minist Transport Key Lab Operat Safety Technol Transport Vehicles Beijing 100088 Peoples R China Changan Univ Sch Automobile Xian Peoples R China

Accurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system generally suffers from the problem of inaccurate height positioning. In this study, a LiDAR and inertial measurement unit (IMU) tightly coupled localization algorithm considering ground constraint is proposed, which is developed based on a pose graph optimization framework. At the front end, the ground segmentation algorithm Patchwork is improved to obtain a point cloud with higher verticality, which is added to the LiDAR inertial odometry. Moreover, constraints are constructed by using current frame ground points and world map ground points, which are added to factor map optimization to limit elevation errors. At the back end, SC++ descriptors are used to construct loop constraints to eliminate accumulated errors. Verifications based on KITTI dataset show that the height positioning accuracy will be improved through introducing ground constraint factor and loop detection factor. Real vehicle tests indicate that the proposed algorithm has better height positioning accuracy and better robustness compared with the LeGO-LOAM algorithm.

关键词： Laser radar Simultaneous localization and mapping optimization Point cloud compression Location awareness Vectors Odometry LiDAR inertial system height positioning pose graph optimization ground constraint

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An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2024年 5卷 617-628页

作者： He, Yuesheng Wang, Tao Chen, Long Zhuang, Hanyang Yang, Ming Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras' extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.

关键词： multi-camera calibration pose graph optimization Automated valet parking automatic calibration automatic calibration pose graph optimization automatic calibration

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Sensor pose Estimation and 3D Mapping for Crane Operations Using Sensors Attached to the Crane Boom

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IEEE ACCESS 2023年 11卷 90298-90308页

作者： Ul Hassan, Mahmood Miura, Jun Toyohashi Univ Technol Dept Comp Sci & Engn Toyohashi 4418580 Japan

This paper describes a method for sensor pose estimation, as well as creating large-scale 3D maps, for construction cranes equipped with a sensor system consisting of a camera, 2D lidar, and IMU. To tackle the challenges posed by the crane boom's complex motion, we utilize an Extended Kalman filter (EKF) to improve the accuracy and reliability of sensor pose estimation. By combining pose estimates from Visual-Inertial Navigation System (VINS) with data from an additional IMU, we estimate the scale value of a monocular camera. This scale value, obtained from the EKF, is then integrated into the VINS algorithm to refine the previously estimated scale value. Slowly rotating 2D lidar is used to build a 3D map. Since there is limited overlap between 2D lidar scans, we leverage the estimated pose to align and construct a comprehensive 3D map. Additionally, we thoroughly evaluate the effectiveness of the latest VINS techniques, as well as the EKF-enhanced VINS approach, in the specific context of crane operations. Through comprehensive performance assessments conducted in both simulated and real environments, we compare the EKF-added VINS method with state-of-the-art VINS techniques. The evaluation results demonstrate that the EKF-added VINS method accurately estimates sensor poses, leading to the generation of high-quality, large-scale 3D point cloud maps for construction cranes.

关键词： Cranes Three-dimensional displays Cameras pose estimation Robot sensing systems Laser radar Kalman filters 3D mapping 2D lidar IMU pose graph optimization complementary filter large crane

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Self-Supervised Deep Visual Odometry Based on Geometric Attention Model

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第3期24卷 3157-3166页

作者： Dai, Jiajia Gong, Xiaoxi Li, Yida Wang, Jun Wei, Mingqiang Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China

Existing learning-based algorithms have a certain potential in visual odometry. In this work, we propose the solution of the learning-based method, which contains the attention mechanism and pose graph optimization. We set a self-supervised network as our backbone to cope with image data and error-heavy estimation pose for pose correction The pre-processing camera poses involved in the network can provide prior information. Combining the advantages of the abundant feature information and efficient attention mechanism, we design a geometric attention module that is sensitive to geometrical structure from images to accurately regress the rotation matrix. Then we improve the loss function with the weights of the attention module to consider the diversity of the data. Experimental results demonstrate the effectiveness and reliability of our approach on the public datasets KITTI with monocular task and stereo task. In comparison, the proposed method is superior to the existing methods in the translation component. In the self-supervised network, learning an attention mechanism can extract an effective connect relation of feature maps. We conduct ablation experiments under the self-supervised network backbone setting different strategies, and conclude that the proposed attention module is applicable to various sequences, and provide loss function improvements on the visual odometry task.

关键词： Visual odometry self-supervised learning attention mechanism pose graph optimization

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