检索结果-内蒙古大学图书馆

DALI-SLAM: Degeneracy-aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization

引用

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2025年 221卷 92-108页

作者： Wu, Weitong Chen, Chi Yang, Bisheng Zou, Xianghong Liang, Fuxun Xu, Yuhang He, Xiufeng Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Wuhan Univ Minist Educ China Engn Res Ctr Spatio temporal Data Smart Acquisit & Wuhan 430079 Peoples R China Wuhan Univ Inst Geospatial Intelligence Wuhan 430079 Peoples R China

LiDAR-Inertial simultaneous localization and mapping (LI-SLAM) plays a crucial role in various applications such as robot localization and low-cost 3D mapping. However, factors including inaccurate motion distortion estimation and pose graph constraints, and frequent LiDAR feature degeneracy present significant challenges for existing LI-SLAM methods. To address these issues, we propose DALI-SLAM, an accurate and robust LI-SLAM that consists of degeneracy-aware LiDAR-inertial odometry (DA-LIO) with a dual spline-based motion distortion correction (DS-MDC) module, and multi-constraint pose graph optimization (MC-PGO). Considering the cumulative errors of micro-electromechanical systems (MEMS) inertial measurement unit (IMU) integration, two continuous-time trajectories in the sliding window are fitted to update the discrete IMU poses for accurate motion distortion correction. In the LiDAR-inertial fusion stage, LiDAR feature degeneracy is detected by analyzing the Jacobian matrix and a remapping strategy is introduced into the updating of error state Kalman Filter (ESKF) to mitigate the influence of degeneracy. Furthermore, in the back-end optimization stage, three types of submap constraints are accurately built with dedicated strategy through a robust variant of the iterative closest point (ICP) method. The proposed method is comprehensively validated using data collected from a helmet-based laser scanning system (HLS) in representative indoor and outdoor environments. Experiment results demonstrate that the proposed method outperforms the SOTA methods on the test data. Specifically, the proposed DS-MDC module reduces trajectory root mean square errors (RMSEs) by 7.9 %, 5.8 %, and 3.1 %, while the degeneracy-aware update strategy achieves additional reductions of 43.3 %, 17.7%, and 4.9 %, respectively, across three typical sequences compared to existing methods, thereby effectively improving trajectory accuracy. Furthermore, the results of DA-LIO demonstrate a maxi

关键词： LiDAR degeneracy LiDAR-inertial SLAM Motion distortion Degeneracy-aware pose graph optimization

来源：评论

学校读者我要写书评

暂无评论

Improving pose graph optimization via Efficient Graduated Non-convexity Scheduling

引用

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第2期23卷 405-417页

作者： Kang, Wonseok Kim, Jaehyun Chung, Jiseong Choi, Seungwon Kim, Tae-wan Seoul Natl Univ SNU Dept Naval Architecture & Ocean Engn Seoul 08826 South Korea Seoul Natl Univ SNU Res Inst Marine Syst Engn Seoul 08826 South Korea Seoul Natl Univ SNU Inst Engn Res Seoul 08826 South Korea

In this study, we propose a novel approach to graduated non-convexity (GNC) and demonstrate its efficacy through its application in robust pose graph optimization, a key component in SLAM backends. Traditional GNC methods rely on heuristic methods for GNC schedule, updating control parameter mu for escalating the non-convexity. However, our approach leverages the properties of convex functions and convex optimization to identify the boundary points beyond which convexity is not guaranteed, thereby eliminating redundant optimization steps in existing methodologies and enhancing both speed and robustness. We demonstrate that our method outperforms the state-of-the-art method in terms of speed and accuracy when used for robust back-end pose graph optimization via GNC. Our work builds upon and enhances the open-source riSAM framework. Our implementation can be accessed from: https://***/SNU-DLLAB/EGNC-PGO.

关键词： Convex optimization graduated non-convexity outlier handling pose graph optimization robust optimization techniques

来源：评论

学校读者我要写书评

暂无评论

Majorization Minimization Methods for Distributed pose graph optimization

引用

IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 22-42页

作者： Fan, Taosha Murphey, Todd D. Meta AI Pittsburgh PA 15213 USA Northwestern Univ Dept Mech Engn Evanston IL 60201 USA

We consider the problem of distributed pose graph optimization (PGO) that has important applications in multirobot simultaneous localization and mapping (SLAM). We propose the majorization minimization (MM) method for distributed PGO ( MM-PGO ) that applies to a broad class of robust loss kernels. The MM--PGO method is guaranteed to converge to first-order critical points under mild conditions. Furthermore, noting that the MM-PGO method is reminiscent of proximal methods, we leverage Nesterov's method and adopt adaptive restarts to accelerate convergence. The resulting accelerated MM methods for distributed PGO-both with a master node in the network ( AMM-PGO(& lowast;) ) and without ( AMM-PGO(#) )-have faster convergence in contrast to the MM--PGO method without sacrificing theoretical guarantees. In particular, the AMM-PGO(#) method, which needs no master node and is fully decentralized, features a novel adaptive restart scheme and has a rate of convergence comparable to that of the AMM-PGO(& lowast;)method using a master node to aggregate information from all the nodes. The efficacy of this work is validated through extensive applications to 2-D and 3-D SLAM benchmark datasets and comprehensive comparisons against existing state-of-the-art methods, indicating that our MM methods converge faster and result in better solutions to distributed PGO.

关键词： Multi-robot systems optimization methods pose graph optimization simultaneous localization and mapping

来源：评论

学校读者我要写书评

暂无评论

pose graph optimization for a MAV Indoor Localization Fusing 5GNR TOA with an IMU 13

Pose Graph Optimization for a MAV Indoor Localization Fusing...

引用

13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Kabiri, Meisam Cimarelli, Claudio Bavle, Hriday Sanchez-Lopez, Jose Luis Voos, Holger Luxembourg Department of Engineering Luxembourg

ISBN: (纸本)9798350320114

This paper explores the potential of 5G new radio (NR) Time-of-Arrival (TOA) data for indoor drone localization under different scenarios and conditions when fused with inertial measurement unit (IMU) data. Our approach involves performing graph-based optimization to estimate the drone's position and orientation from the multiple sensor measurements. Due to the lack of real-world data, we use Matlab 5G toolbox and QuaDRiGa (quasi-deterministic radio channel generator) channel simulator to generate TOA measurements for the EuRoC MAV indoor dataset that provides IMU readings and ground truths 6DoF poses of a flying drone. Hence, we create twelve sequences combining three predefined indoor scenarios setups of QuaDRiGa with 2 to 5 base station antennas. Therefore, experimental results demonstrate that, for a sufficient number of base stations and a high bandwidth 5G configuration, the pose graph optimization approach achieves accurate drone localization, with an average error of less than 15 cm on the overall trajectory. Furthermore, the adopted graph-based optimization algorithm is fast and can be easily implemented for onboard real-time pose tracking on a micro aerial vehicle (MAV). © 2023 IEEE.

关键词： 5G TOA IMU Indoor Localization Micro Aerial Vehicles pose graph optimization QuaDRiGa Sensor Fusion

来源：评论

学校读者我要写书评

暂无评论

Covariance Estimation for pose graph optimization in Visual-Inertial Navigation Systems

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第6期8卷 3657-3667页

作者： Shi, Pengcheng Zhu, Zhikai Sun, Shiying Rong, Zheng Zhao, Xiaoguang Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China NIO Inc Shanghai 201804 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

pose graph optimization helps reduce drift accumulated in pure odometry of visual simultaneous localization and mapping (SLAM) systems by solving a nonlinear least square problem, including both sequential constraints and loop-closing constraints. However, the covariances of all constraints are set to constant matrices or by manual setting. In this paper, we propose a novel approach to approximate covariances of constraints in pose graph optimization to better represent the true uncertainty of the underlying visual-inertial navigation system (VINS) that fuses inertial measurements and visual observations. Specifically, for sequential constraints, we propose to utilize nonlinear factor recovery to optimally extract covariance matrices from the accumulated visual-inertial odometry (VIO). For loop-closing constraints, we propose a dynamic scale estimation method to approximate the scales of the information matrices. To evaluate the effectiveness and robustness of the proposed method, we conduct extensive experiments on public and self-collected datasets in various environments. Results show that our proposed method achieves higher accuracy compared with naively-formulated pose graph optimization adopted by several state-of-the-art visual-inertial navigation systems.

关键词： Covariance estimation loop closing pose graph optimization visual-inertial odometry

来源：评论

学校读者我要写书评

暂无评论

Efficient Graduated Non-Convexity for pose graph optimization 24

Efficient Graduated Non-Convexity for Pose Graph Optimizatio...

引用

24th International Conference on Control, Automation and Systems

ISBN: (纸本)9798331517939;9788993215380

In this study, we propose a novel methodology for Graduated Non-Convexity (GNC) and validate its effectiveness through its application in robust pose graph optimization, which is a critical element in SLAM (Simultaneous Localization and Mapping) backends. Conventional GNC techniques typically depend on heuristic strategies for determining the GNC schedule, specifically in the adjustment of the control parameter mu to increase non-convexity. In contrast, our approach utilizes the characteristics of convex functions and convex optimization to ascertain the boundary points where convexity can no longer be assured. This advancement allows for the elimination of superfluous optimization iterations present in traditional methods, thereby improving both the efficiency and robustness of the process. We provide evidence that our method surpasses the current leading techniques in terms of both speed and accuracy when applied to robust back-end pose graph optimization utilizing GNC. Furthermore, our work builds upon and enhances the open-source riSAM framework. The code is available at: https://***/SNU-DLLAB/EGNC-PGO.

关键词： Graduated Non-Convexity pose graph optimization Convex optimization SIG Kernel Outlier Handling Robust optimization Techniques

来源：评论

学校读者我要写书评

暂无评论

Bathymetric pose graph optimization With Regularized Submap Matching

引用

IEEE ACCESS 2022年 10卷 31155-31164页

作者： Jung, Jongdae Park, Jeonghong Choi, Jinwoo Choi, Hyun-Taek Korea Res Inst Ships & Ocean Engn KRISO Autonomous & Intelligent Maritime Syst Res Div AI Daejeon 34103 South Korea

The quality of sonar seabed mapping performed using autonomous underwater vehicles depends on the accuracy of the vehicle trajectory estimation. To reduce the accumulated pose estimation errors from dead reckoning, bathymetry observations from sonar sensors are often exploited within the framework of pose graph optimization, while the submaps of the seafloor are used to add loop-closure constraints to the pose graph by iterative closest point. However, matching with the submaps suffers from local minima because the seafloor is mostly flat and featureless. To resolve this issue, we regularized the sub-maps to enhance the spatial variations in the vertical direction;thus, we realized improved matching accuracy. Given these constraints, the pose graph can be optimized in real time and provide a corrected trajectory. The performance of the proposed method is validated through experiments using a surface vessel where the same navigation and sonar systems as used in the underwater vehicles are installed, in addition to a GPS receiver for ground truth acquisition.

关键词： Sonar Simultaneous localization and mapping Trajectory Sonar navigation Marine vehicles Global Positioning System Sonar measurements Bathymetric mapping multibeam echosounder pose graph optimization simultaneous localization and mapping unmanned marine vehicles

来源：评论

学校读者我要写书评

暂无评论

Multi-camera Registration with Small FOV Based on pose graph optimization 1

引用

13th International Conference on Green Intelligent Transportation Systems and Safety, GITSS 2022

作者： Chen, Long He, Xing He, Yuesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9789819730520

ISBN: (纸本)9789819730513

Multiple roadside cameras sense and localize autonomous vehicles for Automated Valet Parking. The accurate multi-camera registration into a shared coordinate system is of great importance. Owing to the sparse and distributed placement of multiple cameras, there is a very small field of view (FOV) between the cameras. The multi-camera registration is complex and dependent on human expertise. Therefore, a multi-camera registration method with small FOV based on pose graph optimization is proposed in this paper to automatically generate the extrinsic. The moving checkerboard in the public FOV is utilized as a reference for calibration. The optimization method constructs a pose graph to store camera and checkerboard pose and solves the pose graph by calculating checkerboard reprojection errors. To increase the overall flatness of the ground, ground plane assumptions can be utilized to further optimize the parameters. The experimental results show that the proposed method has greater calibration accuracy on the constructed multiple RGB-D camera system, as well as being simple and efficient in the real calibration procedure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automated valet parking Automatic calibration Multi-camera registration pose graph optimization Small FOV

来源：评论

学校读者我要写书评

暂无评论

Real-time 3D reconstruction using point-dependent pose graph optimization framework

引用

MACHINE VISION AND APPLICATIONS 2022年第2期33卷 1-11页

作者： Liu, Lei Tang, Tianhang Chen, Jie Liu, Yiguang Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

As an essential core of structure from motion, full optimization and pose graph optimization are widely used in most of state-of-the-art 3D reconstruction systems, to estimate the motion trajectory of camera during scanning. Comparing to full optimization, the pose graph optimization has the advantages of low computational complexity and fast convergence, while the practical accuracy of pose graph optimization in applications is intrinsically limited by simple loss function independent of points in scene. In this paper, we proposed a point-dependent pose graph optimization (PDPGO) to address this problem and take it as core to construct a 3D high-precision reconstruction system. In our pipeline, we first construct a hierarchical pose graph by aligning the input frame to its overlapping frames searched by a spatial hashing scheme, which reduces the computational complexity of pairwise alignment. We then derive a loss function of PDPGO from global geometry loss, which improves the accuracy of previous methods. Our system is validated on public benchmarks, and experimental results demonstrate the competing performance against the state-of-the-art systems. And the average reconstruction accuracy in all scenes of ICL-NUIM is up to 0.9 cm.

关键词： 3D reconstruction pose graph optimization RGB-D camera Spatial hashing

来源：评论

学校读者我要写书评

暂无评论

Road-Network-Map-Assisted Vehicle Positioning Based on pose graph optimization

引用

SENSORS 2023年第17期23卷 7581页

作者： Xu, Shuchen Sun, Yongrong Zhao, Kedong Fu, Xiyu Wang, Shuaishuai Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Natl Key Lab Helicopter Aeromech Nanjing 211106 Peoples R China

Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dead-reckoning technology, where a slight positioning error can accumulate over time, resulting in a catastrophic positioning error. Thus, this paper proposes a road-network-map-assisted vehicle positioning method based on the theory of pose graph optimization. This method takes the dead-reckoning result of visual odometry as the input and introduces constraints from the point-line form road network map to suppress the accumulated error and improve vehicle positioning accuracy. We design an optimization and prediction model, and the original trajectory of visual odometry is optimized to obtain the corrected trajectory by introducing constraints from map correction points. The vehicle positioning result at the next moment is predicted based on the latest output of the visual odometry and corrected trajectory. The experiments carried out on the KITTI and campus datasets demonstrate the superiority of the proposed method, which can provide stable and accurate vehicle position estimation in real-time, and has higher positioning accuracy than similar map-assisted methods.

关键词： visual odometry road network map map correction points optimization and prediction model pose graph optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：