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Decimeter-level vehicular positioning in urban environments with factor graph-based PPP and robust regression

GPS Solutions 2025年第3期29卷 1-18页

作者： Li, Xin Wu, Feiyang Chang, Hanyu Li, Xingxing Tan, Yuxuan Shen, Zhiheng Zhang, Xiaohong School of Geodesy and Geomatics Wuhan University Wuhan China China Ship Development and Design Center Wuhan China Chinese Antarctic Center of Surveying and Mapping Wuhan University Wuhan China

The development of the Internet of Things and autonomous driving has significantly increased the demand for high-precision and robust vehicle positioning in modern intelligent transport systems. Nowadays, precise point positioning (PPP) technology is recognized as an important approach for worldwide accurate positioning services with Global Navigation Satellite Systems (GNSS). However, traditional PPP methods, which typically rely on the extended Kalman filter (EKF) for state estimation at a single time step, struggle to deliver satisfactory performance in urban canyons, where GNSS measurements are often affected by multipath and non-line-of-sight signals. In this contribution, we propose a PPP method based on a factor graph optimization (FGO) framework associated with robust regression, aiming to enhance the positioning accuracy and robustness of vehicular navigation in urban areas. The FGO estimator fully exploit the historical information by incorporating multi-epoch raw code and phase observations in a sliding window and correlates multi-epoch state parameters using a marginalization strategy. In particular, to mitigate the impact of outliers in urban environments, the Huber regression model, which combines the advantages of the mean squared error (MSE) and mean absolute error models, is introduced in parameter estimation. Under the FGO framework, the impact of multi-window processing and Huber regression on parameter estimation is carefully investigated and discussed. In scenarios with generally open conditions, the proposed FGO-based PPP algorithm demonstrates a gradual reduction in positioning errors during the PPP convergence process, with an increase in window size. When the window size reaches 20 s, this method achieves a 46.6% improvement compared to the EKF-based PPP method. In obstructed environments, the Huber regression plays an important role in the suppression of outliers, and the 3D RMSE of positioning errors is improved from 1.22 to 0.51

关键词： Extended Kalman filter factor graph optimization GNSS Huber regression model PPP

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UVIO: Adaptive Kalman Filtering UWB-Aided Visual-Inertial SLAM System for Complex Indoor Environments

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REMOTE SENSING 2024年第17期16卷 3245页

作者： Li, Junxi Wang, Shouwen Hao, Jiahui Ma, Biao Chu, Henry K. Hong Kong Polytech Univ Dept Mech Engn Hong Kong 999077 Peoples R China

Precise positioning in an indoor environment is a challenging task because it is difficult to receive a strong and reliable global positioning system (GPS) signal. For existing wireless indoor positioning methods, ultra-wideband (UWB) has become more popular because of its low energy consumption and high interference immunity. Nevertheless, factors such as indoor non-line-of-sight (NLOS) obstructions can still lead to large errors or fluctuations in the measurement data. In this paper, we propose a fusion method based on ultra-wideband (UWB), inertial measurement unit (IMU), and visual simultaneous localization and mapping (V-SLAM) to achieve high accuracy and robustness in tracking a mobile robot in a complex indoor environment. Specifically, we first focus on the identification and correction between line-of-sight (LOS) and non-line-of-sight (NLOS) UWB signals. The distance evaluated from UWB is first processed by an adaptive Kalman filter with IMU signals for pose estimation, where a new noise covariance matrix using the received signal strength indicator (RSSI) and estimation of precision (EOP) is proposed to reduce the effect due to NLOS. After that, the corrected UWB estimation is tightly integrated with IMU and visual SLAM through factor graph optimization (FGO) to further refine the pose estimation. The experimental results show that, compared with single or dual positioning systems, the proposed fusion method provides significant improvements in positioning accuracy in a complex indoor environment.

关键词： ultra-wideband (UWB) visual-inertia SLAM RSSI-EOP factor-related gain NLOS identification and correction adaptive Kalman filter factor graph optimization

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Virtual-network-reconstruction-based underwater acoustic localization framework: Theory and experiment validation

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OCEAN ENGINEERING 2024年 297卷

作者： Liu, Feng Chen, Huifang Li, Yida Xie, Lei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Zhoushan Ocean Res Ctr Zhoushan 316021 Zhejiang Peoples R China

Due to the harsh underwater environment, it is a challenging issue to realize the high -precision localization for underwater mobile nodes. In this paper, we investigate the problem of asynchronous acoustic localization for underwater mobile nodes, and propose a virtual -network -reconstruction -based underwater acoustic localization framework using a factor graph, named as the VNR-UAL-FG framework. Integrating the beacon network topology and positioning message broadcasting timing, a virtual beacon network is reconstructed incorporating the motion of underwater mobile node and the historical received positioning messages. The VNR-UAL-FG framework consists of three essential modules. The input pre-processing and state estimation module handles data collected from various sensors and conducts state estimation as a motion factor. The estimation module focuses on the acoustic -aided localization to perform a joint localization and time synchronization as an acoustic factor. The optimization module integrates motion factors and acoustic factor to refine the localization results. The effectiveness of proposed framework was validated by simulations and sea experiment. Numerical results show that localization accuracy is improved by more than 60% and 25% compared with modified dead -reckoning method and UKF-based localization framework. And the localization update rate is more than 2 times compared with TDoA-based localization method.

关键词： Underwater localization Underwater acoustic-aided localization Underwater mobile node factor graph optimization Asynchronous

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factor graph-based PPP-RTK for accurate and robust positioning in urban environments

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JOURNAL OF GEODESY 2024年第3期98卷 21-21页

作者： Li, Xin Li, Xingxing Wang, Xuanbin Chang, Hanyu Tan, Yuxuan Shen, Zhiheng Wuhan Univ Sch Geodesy & Geomat Hubei Luojia Lab 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China

The PPP-RTK system, which is capable of providing a centimeter-level real-time positioning service for an unlimited number of users, is becoming a promising tool in mass-market applications such as smartphones, the Internet of Things (IoT), and the automotive industry. The extended Kalman filter (EKF) is the conventional method for parameter estimation in the existing PPP-RTK system. Recently, an alternative method known as factor graph optimization (FGO), which fully leverages the time correlation among current and historical measurements, has the potential to further improve the accuracy and robustness of PPP-RTK solutions. In this contribution, a factor graph optimization-based PPP-RTK framework is developed, where raw pseudorange, phase measurements, precise atmospheric corrections, and time-differenced carrier-phase (TDCP) measurements serve as factors in FGO estimators. The continuously tracked phase ambiguities are estimated as the time-invariant state node and propagated by marginalization while ambiguity resolution is conducted independently between epochs. A second optimization process with the utilization of ambiguity-resolved solutions and time-differenced carrier-phase (TDCP) measurements is conducted to further improve the reliability of positioning results. The effectiveness of the proposed method is evaluated by vehicular tests in urban environments. Results indicate that the FGO method could improve the performance of ambiguity resolution by reducing the ambiguity search space and increasing the ratio values, leading to a significant accuracy improvement of 55% in an open-sky environment compared to the traditional EKF-based method. Furthermore, in GNSS signal partly block scenes, the FGO-based PPP-RTK is capable of obtaining more robust and accurate positioning solutions with fewer outliers compared to the EKF method.

关键词： PPP-RTK factor graph optimization Extended Kalman filter Ambiguity resolution Time-differenced carrier phase

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RTK-LIO: Tightly-Coupled RTK/LiDAR/Inertial Navigation System Based on optimization Approach

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IEEE Sensors Journal 2025年

作者： Wang, Rongtian Zhang, Yuqi Li, Tao Wang, Chao Wu, Qi Pei, Ling Zhang, Wen-An Zhejiang University of Technology College of Information Engineering Zhejiang China Shanghai Huace Navigation Technology Ltd Shanghai China Shanghai Jiao Tong University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai China

Global Navigation Satellite System Real-Time Kinematic (GNSS-RTK) serves as a vital tool for providing absolute positioning for autonomous systems. However, its performance suffers considerable degradation in urban canyon environments due to the well-known challenges caused by multipath effects and Non-Line-of-Sight (NLOS). Light Detection and Ranging (LiDAR)/Inertial Odometry (LIO) offers high-precision local pose estimation in structured urban settings, but it tends to accumulate drift over time. Recognizing their complementary strengths, this paper proposes an adaptive integration of GNSS-RTK with LIO to achieve continuous and precise global positioning for autonomous systems in urban environments. The raw data is modeled and optimized within the framework of a factor graph. At the same time, double difference carrier phase and ambiguity are added to the estimated states. Finally, RTK-LIO is evaluated on public datasets. It greatly exceeds the benchmarks (LIGO, GLIO and RTK) in both accuracy and smoothness. © 2001-2012 IEEE.

关键词： factor graph optimization LiDAR/Inertial Odometry RTK Sensor Fusion Tightly-Coupled Navigation System

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A new geometric approach for three view line reconstruction and motion estimation in Manhattan Scenes 18

A new geometric approach for three view line reconstruction ...

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18th Conference on Robots and Vision (CRV)

作者： Thatavarthy, Ayyappa Swamy Sharma, Tanu Krishna, K. Madhava IIIT Robot Res Ctr Hyderabad India

ISBN: (纸本)9781665414135

Owing to the inherent geometry, the extent of map reconstructed using line-based SfM(structure from motion) is superior to point-based SfM. However, with the existing methods, estimation of structure and motion from observed 2D line segments in images is more complex than that of points. To overcome this, we propose a simple and robust 1-parameter approach for Structure and Motion Estimation from line features in Manhattan Scenes from three views. We leverage the vanishing point directions to estimate the relative rotations as well as to fix the 3D line direction. In consequence we build a constraints matrix, which has the relative translations and 3D line depth as its null space. We then perform 1-parameter line BA using factor graph based cost function. We compare the efficacy of our method with standard line triangulation in synthetic as well as real-world scenes.

关键词： manhattan scenes line features Structure and motion estimation vanishing points vanishing line Plucker coordinates orthonormal representation factor graph optimization

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GO-INO: graph optimization MEMS-IMU/NHC/Odometer Integration for Ground Vehicle Positioning

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MICROMACHINES 2022年第9期13卷 1400-1400页

作者： Zhu, Kai Yu, Yating Wu, Bin Jiang, Changhui Jiangsu Univ Technol Sch Automobile & Traff Engn Changzhou 213001 Peoples R China Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Finnish Geospatial Res Inst Dept Remote Sensing & Photogrammetry FI-0245 Espoo Finland

Global navigation satellite system (GNSS) and inertial navigation system (INS) are indispensable for ground vehicle position and navigation. The Kalman filter (KF) is the first choice to integrate them and output more reliable navigation solutions. However, the GNSS signal is denied in urban areas, i.e., tunnels, and the INS position errors diverge quickly over time. Under normal conditions, the ground vehicle will not slide or jump off the ground;nonholonomic constraints (NHC) and odometers are available to aid the INS and reduce its position errors. factor graph optimization (FGO) recently attracted attention as an advanced sensor fusion algorithm. This paper implemented the FGO method based on GNSS/INS/NHC/Odometer integration. In the FGO, state transformation, measurement model, the NHC, and the odometer were all regarded as constraints employed to construct a graph;an iterative process was utilized to find the optimal estimation results. Two experiments were carried out: firstly, the FGO-GNSS/INS performance was assessed and compared with the KF-GNSS/INS;secondly, we compared the FGO-GNSS/INS/NHC/Odometer and KF-GNSS/INS/NHC/Odometer under GNSS denied environments. Experimental results supported that the FGO improved the performance.

关键词： GNSS INS signal outage factor graph optimization

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Monocular Semantic Mapping Based on 3D Cuboids Tracking 53

Monocular Semantic Mapping Based on 3D Cuboids Tracking

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Ji, Xingwu Gong, Zheng Miao, Ruihang Xue, Wuyang Ying, Rendong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728192017

Semantic mapping based on information of objects has become a crucial component for the surrounding comprehension and the more robust navigation. In this paper, we propose a system for simultaneous localization and mapping (SLAM) that combines multiple objects tracking and factor graph optimization with semantically meaningful landmarks to achieve accurate monocular semantic mapping. Firstly, the process of object recognition uses a vanishing point sampling-based approach to efficiently infer the class and position of object landmarks from 2D bounding box object detection. Secondly, The semantic frontend utilizes local matching-based data association to track raw cuboid proposals. It can provide semantic constraints to reduce cuboid scale drift and improve its position estimation. Finally, we present a multi-view factor graph optimization which can use motion modal of the camera to optimize stable cuboids. The semantic mapping experiments on our own built virtual scene show better accuracy and robustness over existing approaches. We evaluate the effectiveness of our approach on public KITTI datasets and a real scene.

关键词： visual simultaneous localization and mapping (SLAM) 3D semantic map factor graph optimization

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Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas

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IEEE ACCESS 2020年 8卷 124323-124338页

作者： Wen, Weisong Bai, Xiwei Zhang, Guohao Chen, Shengdong Yuan, Feng Hsu, Li-Ta Hong Kong Polytech Univ Intelligent Positioning & Nav Lab IPNL Hong Kong Peoples R China Guangzhou Chinese Acad Sci Inst Software Applicat Technol Guangzhou 511458 Peoples R China

Achieving accurate and reliable positioning in dynamic urban scenarios using low-cost vehicular onboard sensors, such as the global navigation satellite systems (GNSS), camera, and inertial measurement unit (IMU), is still a challenging problem. Multi-Agent collaborative integration (MCI) opens a new window for achieving this goal, by sharing the sensor measurements between multiple agents to further improve the accuracy of respective positioning. One of the major difficulties in MCI is to effectively connect all the sensor measurements arising from multiple independent agents. The popular approach is to find the overlapping areas between agents using active sensors, such as cameras. However, the performance of overlapping area detection is significantly degraded in outdoor urban areas due to the challenges arising from numerous unexpected moving objects and unstable illumination conditions. To fill this gap, this paper proposes to leverage both the camera-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and brings the MCI to outdoor urban scenarios using low-cost onboard sensors. Moreover, a novel MCI framework is proposed to integrate the sensor measurements from the low-cost GNSS receiver, camera, IMU, and inter-ranging using state-of-the-art factor graph optimization (FGO) to fully explore their complementary properties. The proposed MCI framework is validated using two challenging datasets collected in urban canyons of Hong Kong. We conclude that the proposed MCI framework can effectively improve the positioning accuracy of the respective agents in the evaluated datasets. We believe that the proposed MCI framework has the potential to be prevalently adopted by the connected intelligent transportation systems (ITS) applications to provide robust positioning using low-cost onboard sensors in urban scenarios.

关键词： Multi-agent collaborative positioning GNSS camera INS inter-ranging factor graph optimization urban canyons

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SLAM integrated mobile mapping system in complex urban environments

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年 166卷 316-332页

作者： Li, Shuaixin Li, Guangyun Wang, Li Qin, Yuchu PLA Informat Engn Univ Dept Geospatial Informat Zhengzhou 450001 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China

In this paper, a SLAM-integrated MMS with the capability of efficient, consistent and robust mapping in complex environments where GNSS signal is intermittently lost is presented. The system is developed to take the feedback from mapping module to the localization module into account, which significantly improves the smoothness of the 6 DOF trajectory estimation. In particular, the complex mapping problem is modeled as a hierarchical factor graph to improve the computation efficiency of the optimization back-end. Moreover, GNSS signal is modeled as a plug-and-play constrained factor to facilitate the mapping process in complex urban environments. We evaluate the performance of the proposed method on KITTI benchmark, and compare the result with both the GNNS/INS system and SuMa, which represents the traditional MMS and the typical laser SLAM system respectively. The results confirm that the proposed method enables normal and accurate mapping even when the inavailability of GNSS is up to 60%. It outperforms the traditional MMS in terms of map consistency with an increase of 24.7% on average MME, and outperforms laser SLAM in terms of the absolute accuracy with 5.9 times increase on average RMSE.

关键词： Mobile mapping SLAM factor graph optimization Seamless positioning Data fusion

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