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14th International Conference on Indoor Positioning and Indoor Navigation

作者： Su, Meiling Wang, Bing Ahad, Sugata Huang, Xinyue Zhang, Guohao Hsu, Li-Ta Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Hong Kong Peoples R China

ISBN: (纸本)9798350366419;9798350366402

Existing positioning methods have limitations in accuracy and reliability for seamless positioning. Global Navigation Satellite System (GNSS) faces multipath and signal blockage issues, especially indoors. Wi-Fi positioning solutions are mostly restricted to indoors due to large infrastructure requirements. Infrastructure-independent positioning systems, such as visual-inertial navigation systems (VINS), are limited to providing relative pose estimation and are affected by environmental luminance. This paper presents an approach for smartphone seamless positioning by adaptively fusing multisensor data from GNSS, Wi-Fi Round Trip Time (RTT) and VINS using factor graph optimization (FGO). The adaptive weighted FGO algorithm optimizes the estimation of the state variables by minimizing the loss function of selected factors with scaled covariance. Combining the strengths of GNSS, Wi-Fi RTT, and VINS, the proposed system achieves improved positioning accuracy and robustness, and experimental results demonstrate our method's effectiveness in various scenarios.

关键词： sensor fusion smartphone seamless positioning factor graph optimization Wi-Fi RTT visual positioning system GNSS positioning

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L-VIWO: Visual-Inertial-Wheel Odometry based on Lane Lines

L-VIWO: Visual-Inertial-Wheel Odometry based on Lane Lines

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhao, Bin Zhang, Yunzhou Huang, Junjie Zhang, Xichen Long, Zeyu Li, Yulong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

To achieve precise localization for autonomous vehicles and mitigate the problem of accumulated drift error in odometry, this paper proposes L-VIWO, a Visual-Inertial-Wheel Odometry based on lane lines. This method effectively utilizes the lateral constraints provided by lane lines to eliminate and relieve the incrementally accumulated pose errors. Firstly, we introduce a lane line tracking method that enables multi-frame tracking of the same lane line, thereby obtaining multi-frame data of a lane line. Then, we utilize multi-frame data of the lane lines and the curvature characteristics of adjacent lane lines to optimize the positions of the lane line sample points, thus building a reliable lane line map. Finally, we use the built local lane line map to correct the position of the vehicle. Based on the corrected position and prior pose from the odometry, we build a graph optimization model to optimize the pose of the vehicle. Through localization experiments on the KAIST dataset, it has been demonstrated that the proposed method effectively enhances the localization accuracy of odometry, thus confirming the effectiveness of the method.

关键词： Visual-inertial-wheel odometry lane lines factor graph optimization

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STATE ESTIMATION IN MULTI-SENSOR FUSION NAVIGATION: EQUIVALENCE ANALYSIS ON FILTERING AND optimization 5

STATE ESTIMATION IN MULTI-SENSOR FUSION NAVIGATION: EQUIVALE...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Xu, Zhuo Zhu, Feng Zhang, Xiaohong Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Wuhan Univ Chinese Antarctic Ctr Surveying & Mapping Wuhan 430079 Peoples R China

Integration of information from multi-sensor can provide navigation systems with reliable estimates of their own states, which overcomes shortage of standalone sensors. State estimation approaches, which can be categorized into filtering-based and optimization-based methods, provide the means to fuse information from various sensors with different principles to estimate the system's position, orientation, and other navigation parameters accurately. Recent researches have shown that optimization-based frameworks outperform filtering-based ones in terms of accuracy. However, both methods are based on maximum likelihood estimation (MLE) and, assuming Gaussian noise, should be theoretically equivalent. In this paper, we comprehensively and theoretically analyse the differences between the two methods, including algorithms and strategies. Our simulated experiments based on visual odometry (VO) indicate that filtering-based approaches are equal to optimization-based ones in accuracy when employing the same strategies and under the premise of the same measurements and observation model. Therefore, future research on sensor-fusion navigation problems should concentrate on strategies rather than state estimation methods.

关键词： Multi-Sensor Fusion State Estimation Extended Kalman Filter factor graph optimization Visual Odometry

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LIDAR-INERTIAL LOCALIZATION WITH GROUND CONSTRAINT IN A POINT CLOUD MAP 5

LIDAR-INERTIAL LOCALIZATION WITH GROUND CONSTRAINT IN A POIN...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Ai, Mengchi Hokmabadi, Ilyar Asl Sabbaghian Elhabiby, Mohamed Moussa, Mohamed Zekry, Ahmed Mohamed, Ahmed El-Sheimy, Naser Univ Calgary Dept Geomat Engn 2500 Univ Dr NW Calgary AB T2N1N4 Canada Micro Engn Tech Inc Calgary AB Canada

Real-time localization is a crucial task in various applications, such as automatic vehicles (AV), robotics, and smart city. This study proposes a framework for map-aided LiDAR-inertial localization, with the objective of accurately estimating the trajectory in a point clouds map. The proposed framework addresses the localization problem through a factor graph optimization (FGO), enabling the fusion of homogenous measurements for sensor fusion and designed absolute and relative constraints. Specifically, the framework estimates the light detection and ranging (LiDAR) odometry by leveraging inertial measurement unit (IMU) and registering corresponding featured points. To eliminate the accumulative error, this paper employs a ground plane distance and a map matching error to constraint the positioning error along the trajectory. Finally, local odometry and constraints are integrated using a FGO, including LiDAR odometry, IMU pre-integration, and ground constraints, map matching constraints, and loop closure. Experimental results were evaluated on an open-source dataset, UrbanNav, with an overall localization accuracy of 2.29 m (root mean square error, RMSE).

关键词： Map-aided localization factor graph optimization Point cloud map Ground constraints Sensor fusion

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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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A robust visual SLAM system for low-texture and semi-static environments

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第22期83卷 61559-61583页

作者： He, Bin Xu, Sixiong Dong, Yanchao Wang, Senbo Yue, Jiguang Ji, Lingling Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Shanghai Res Inst Intelligent Autonomous Syst Shanghai 200120 Peoples R China

Precisely estimating a vehicle's pose in a prior map is a fundamental capability for autonomous driving. This task, however, remains challenging in low-texture and semi-static environments where the number of available features is limited and their attached objects are faced with the risk of positional change over time. To cope with the adverse conditions, we propose an innovative visual SLAM method that can correct the position of biased objects in the prior map besides estimating the accurate camera pose. Our approach is based upon the structure of independent object management to complete localization and mapping. Each object in the current frame is represented by a group of organized raster points. Owing to the adaptive pixel-based object rasterization, these raster points are more dependable to use in the visual SLAM pipeline. Assisted by the factor graph optimization, each object can be separately supervised although with multi-constraints from surroundings, resulting in robust tracking and map correction. With the benefit of GPU acceleration in data association, the execution time is sharply reduced. The proposed algorithm is validated in computer graphic render datasets.

关键词： Visual SLAM Low-texture and semi-static environments Adaptive object rasterization GPU acceleration factor graph optimization

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Integrating Intensity Information for Three-stage Lidar Loop Closure Detection 6

Integrating Intensity Information for Three-stage Lidar Loop...

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6th International Conference on Robot Systems and Applications (ICRSA)

作者： Tu, Yuyang Zhang, Yanduo Lu, Tao Li, Xun Jiang, Weiyang Liu, Bo Hubei Three Gorges Lab Wuhan Peoples R China Hubei Univ Arts & Sci Wuhan Peoples R China CECK Intelligent Connect Wuhan Co Ltd Wuhan Peoples R China Wuhan Inst Technol Sch Comp Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798400708039

Mainstream loop closure detection based on geometric descriptors struggles to guarantee high detection accuracy in complex environments and requires significant computational resources. To enhance the accuracy of loop closure detection while reducing computational overhead, this paper fully leverages the geometric and dense intensity features obtained from lidar scans to develop a global descriptor that effectively captures the geometric and intensity structures of the environment. An efficient three-stage hierarchical retrieval strategy is employed, including K-Nearest Neighbor search constraints within the ikd-tree and fast geometric and intensity structure reidentification through binary operations. All constraints are globally optimized using factor graphs. The proposed method is evaluated on publicly available datasets, and the results demonstrate that our approach achieves higher trajectory accuracy.

关键词： SLAM Loop Closure Detection factor graph optimization

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Monocular Visual-Inertial Odometry with Wheel Encoder for Urban Vehicle 3

Monocular Visual-Inertial Odometry with Wheel Encoder for Ur...

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3rd International Conference on Sensors and Information Technology (ICSI)

作者： Qiu, Yue Li, Yong Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781510666122;9781510666139

Visual-inertial navigation system (VINS) is subject to degradation in urban vehicles. Specific motion patterns of the car and dynamic objects considerably reduce the pose estimation accuracy of VINS. A tightly-coupled visual-inertial-wheel odometry based on factor graph optimization with sliding window for ground vehicles is proposed in this paper. Wheel encoder and non-holonomic constraint information are utilized in pre-integration as an additional constraint to assist scale estimation. To promote the robustness of the system in complex environments, the prior pose from dead reckoning is employed to assist feature tracking and dynamic object filtering. The results from the real-world experiments in large-scale urban driving environments show better robustness and accuracy compared to the state-of-the-art methods.

关键词： multisensory fusion navigation visual-inertial navigation system wheel encoder factor graph optimization

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Comparison of FGO and KF for PDR-GNSS Fusion on Smartphone for Diverse Pedestrian Movements 13

Comparison of FGO and KF for PDR-GNSS Fusion on Smartphone f...

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

作者： Hussain, Amjad Diez, Luis Enrique University of Deusto Faculty of Engineering Avda. Universidades 24 Bilbao48007 Spain

ISBN: (纸本)9798350320114

Smartphone-based positioning has gained significant popularity due to its widespread availability. However, its reliance on GNSS as the primary positioning component poses limitations in challenging environments such as urban canyons, tunnels, and indoor areas, where GNSS signals are distorted or missed due to multi-path interference and non-line-of-sight (NLOS). To address this, sensor fusion techniques have been employed, with the integration of MEMS IMU being a common approach due to its complementary features with GNSS. For pedestrian localization, the fusion of PDR and GNSS is a classic architecture that commonly involves the use of the Kalman Filter (KF). Recently, researchers have started to consider the factor graph optimization (FGO) algorithm for the fusion, due to its better accuracy. However, it is still necessary to asses its performance compared to the KF under different situations, such as various pedestrian movements. In this study, experimental evaluations during walking and running activities demonstrate that FGO consistently outperforms KF, resulting in a significant reduction of mean horizontal errors by 30.6% and 10% in walking and running, respectively. These findings highlight that between walking and running modes, the accuracy of FGO is influenced by the magnitude of PDR errors and the level of correlation in GNSS measurements, resulting in varying performance. Overall, FGO demonstrated superior efficacy compared to KF in enhancing the accuracy and reliability of smartphone-based positioning systems, especially in challenging pedestrian scenarios. © 2023 IEEE.

关键词： factor graph optimization GNSS Kalman Filter PDR Smartphone

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A SLAM Method for Handheld Hemispherical View Laser Scanning System 14

A SLAM Method for Handheld Hemispherical View Laser Scanning...

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ISPRS TC III and TC IV 14th GeoInformation for Disaster Management (Gi4DM)

作者： Duan, Xuzhe Yang, Honggang Hu, Qingwu Wu, Xuan Zhao, Pengcheng Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China China Railway Siyuan Survey & Design Grp Co LTD Wuhan Peoples R China

In view of the problem that some multi-line light detection and ranging (LiDAR) scans a small field of view in the vertical direction, a framework that uses an integrated handheld hemispherical view LiDAR and inertial measurement unit (IMU) scanning system for simultaneous localization and mapping (SLAM) is proposed. For the structural characteristics of the hemispherical view LiDAR scan lines, a ground segmentation pre-processing module based on seed points is designed. The segmented ground points are downsampled to eliminate redundant vertical constraints. The IMU data and the pre-processed point cloud are performed state estimation via a tightly coupled iterative Extended Kalman Filter (iEKF) to obtain the real-time poses. The detected loop closures provide global constraints for the point cloud map. The factor graph is used to process the back-end optimization incrementally to eliminate the accumulation error of the system. Data from diverse scenes are collected via a prototype system. Both qualitative and quantitative experiments are performed to prove the accuracy and performance of the framework. Experiments show that our framework outperforms the state-of-the-art SLAM methods for the hemispherical view LiDAR-IMU integrated scanning system.

关键词： SLAM 3D point cloud iEKF factor graph optimization laser scanning system

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