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TSF-GINS: Based on time-fixed sliding window with factor graph a global navigation satellite system and inertial measurement unit tightly coupled localization system

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MEASUREMENT 2025年 239卷

作者： Zheng, Xiaoni Dong, Yanpeng Zhao, Yufei Zhang, Baosheng Li, Mengfan Beijing Inst Mech Equipment Beijing 100854 Peoples R China

High-precision localization is fundamental for the unmanned platforms. Existing integrated navigation systems are designed for optimal vehicular operation in environments, rather than for conditions of Global Navigation Satellite System (GNSS) unavailability. In this paper, we propose a Global Navigation Satellite System and Inertial Measurement Unit (IMU) tightly coupled localization system, TSF-GINS, based on the time-fixed sliding window with a factor graph. Initially, TSF-GINS preprocesses the input sensor data to achieve a unified coordinate system. Subsequently, the time-fixed sliding window enables temporal alignment of GNSS and IMU, marginalizing oldest frames and insert the newest one. A factor graph optimization framework is introduced to estimate highly accurate poses by fusing preintegrated IMU measurements and GNSS observations. To ensure localization accuracy, we compare against other state-of-the-art algorithms on public datasets and real-world environment. The results demonstrate that our method is able to achieve better localization accuracy and robustness.

关键词： Positioning GNSS-IMU tightly coupled localization system factor graph optimization Sliding window

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graph-Based Indoor 3D Pedestrian Location Tracking With Inertial-Only Perception

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第5期24卷 4481-4495页

作者： Bai, Shiyu Wen, Weisong Su, Dongzhe Hsu, Li-Ta Hong Kong Polytech Univ Hong Kong Peoples R China Hong Kong Appl Sci & Technol Res Inst ASTRI Hong Kong Peoples R China

Pedestrian location tracking in emergency responses and environmental surveys of indoor scenarios tend to rely only on their own mobile devices, reducing the usage of external services. Low-cost and small-sized inertial measurement units (IMU) have been widely distributed in mobile devices. However, they suffer from high-level noises, leading to drift in position estimation over time. In this work, we present a graph-based indoor 3D pedestrian location tracking with inertial-only perception. The proposed method uses onboard inertial sensors in mobile devices alone for pedestrian state estimation in a simultaneous localization and mapping (SLAM) mode. It starts with a deep vertical odometry-aided 3D pedestrian dead reckoning (PDR) to predict the position in 3D space. Environment-induced behaviors, such as corner-turning and stair-taking, are regarded as landmarks. Multi-hypothesis loop closures are formed using statistical methods to handle ambiguous data association. A factor graph optimization fuses 3D PDR and behavior loop closures for state estimation. Experiments in different scenarios are performed using a smartphone to evaluate the performance of the proposed method, which can achieve better location tracking than current learning-based and filtering-based methods. Moreover, the proposed method is also discussed in different aspects, including the accuracy of offline optimization and proposed height regression, and the reliability of the multi-hypothesis behavior loop closures. The video (YouTube) or (BiliBili) is also shared to display our research.

关键词： Simultaneous localization and mapping Pedestrians Accuracy Three-dimensional displays optimization Stairs Reliability Mobile handsets Location awareness Dead reckoning Indoor localization pedestrians inertial perception SLAM factor graph optimization

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Application of Semantic Information Module in LiDAR-Based Simultaneous-Localization-and- Mapping Algorithm

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IEEE ACCESS 2025年 13卷 43398-43413页

作者： Huang, Shihong Ma, Dalin Wu, Jiayang Lin, Caihou Liu, Jinhui Hu, Cheng Guangzhou Univ Sch Phys & Mat Sci Dept Optoelect Engn Guangzhou 510006 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Dept Robot Guangzhou 510006 Peoples R China

Autonomous driving and robotics require precise perception of the surrounding environment. Therefore, the simultaneous-localization-and-mapping algorithm, which is used for accurate pose estimation and environmental perception, is of great importance. LiDAR, with its high measurement accuracy and ability to work around the clock, is widely used as the main sensor for simultaneous-localization-and-mapping algorithms. As the mass production technology of LiDAR becomes increasingly mature, various LiDAR based simultaneous-localization-and-mapping algorithms have been proposed one after another. In this paper, comparative experiments are conducted on the latest excellent algorithms, on this basis, a high-precision and robust LiDAR-based simultaneous-localization-and-mapping algorithm suitable for different scenarios is proposed. This algorithm combines the simultaneous-localization-and-mapping algorithm with the Scan Context semantic descriptor. Firstly, in the front end of the algorithm, the iterative Kalman filter is used for point cloud scan registration. In the back end, factor graph optimization is adopted to reduce global trajectory drift. Secondly, the algorithm performs loop closure detection from coarse to fine through Scan Context and the iterative closest point algorithm. Finally, the environmental map generated by the algorithm is managed by an incremental kd-tree. The evaluation experiments show that the algorithm proposed in this paper has significantly improved accuracy and robustness, which promotes the wide application of algorithms in different scenarios.

关键词： Laser radar Simultaneous localization and mapping Point cloud compression Accuracy optimization Odometry Visualization Robustness Odometers Heuristic algorithms iterative Kalman filter factor graph optimization scan context

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IC-GLI: a real-time, INS-centric GNSS-LiDAR-IMU localization system for intelligent vehicles

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 026314-026314页

作者： Zhang, Yong Li, Xudong Zhang, Wei Zhao, Fengkui Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China Special Equipment Safety Supervis Inspection Inst Wujiang Branch Suzhou 215200 Peoples R China

The integration of multiple sensors plays a vital role in achieving accurate vehicle positioning. However, light detection and ranging (LIDAR) and global navigation satellite system (GNSS) are susceptible to environmental effects, unlike inertial navigation system (INS), which remains unaffected by external factors. To enhance the precise positioning of intelligent vehicles in diverse environments and maximize the use of inertial measurement unit (IMU) data, this study presents a LIDAR, IMU, and GNSS-based positioning method centered around INS. The proposed method employs an IMU pre-integration model that includes the Earth's rotation and utilizes a factor graph optimization framework to fuse the measurement data from LIDAR, IMU, and GNSS. By assigning appropriate weights to the residuals and dynamically adjusting the contributions of each sensor based on the prevailing environmental conditions, the optimal localization results are obtained through the solution of the maximum a posteriori estimate and the update of the INS state. Finally, the proposed system's localization performance is validated using intelligent vehicles in the Robot Operating System framework. The results demonstrate that the system achieves high accuracy and robustness across various environments.

关键词： multi-source fusion positioning factor graph optimization intelligent vehicles IMU GNSS LiDAR

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BA-LINS: A Frame-to-Frame Bundle Adjustment for LiDAR-Inertial Navigation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 6621-6634页

作者： Tang, Hailiang Zhang, Tisheng Wang, Liqiang Yuan, Man Niu, Xiaoji Wuhan Univ GNSS Res Ctr Wuhan 430079 Peoples R China Hubei Luojia Lab Wuhan 430079 Peoples R China

Bundle adjustment (BA) has been proven to improve the accuracy of the LiDAR mapping, but has not yet been properly employed in a dead-reckoning navigation system. In this paper, we present a frame-to-frame (F2F) BA for LiDAR-inertial navigation, named BA-LINS. Based on the direct F2F point-cloud association method, the same-plane points are associated among the LiDAR keyframes. Hence, the F2F plane-point BA measurement model can be constructed using the same-plane points. The LiDAR BA and the inertial measurement unit (IMU)-preintegration measurements are tightly coupled under the framework of factor graph optimization. Meanwhile, an effective adaptive covariance estimation algorithm for LiDAR BA measurements is proposed to improve the accuracy further. Exhaustive experiment results on public and private datasets demonstrate that BA-LINS yields superior accuracy to state-of-the-art methods. Compared to the baseline system FF-LINS, the absolute translation accuracy and state-estimation efficiency of BA-LINS are improved by 29.5% and 28.7%, respectively. Besides, the proposed adaptive covariance estimation algorithm exhibits notably improved accuracy and robustness.

关键词： Laser radar Navigation Accuracy Feature extraction State estimation Odometry Thickness measurement Point cloud compression optimization Lips Bundle adjustment LiDAR-inertial navigation factor graph optimization multi-sensor fusion navigation

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Continuous High-Precision Positioning in Smartphones by FGO-Based Fusion of GNSS-PPK and PDR

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MICROMACHINES 2024年第9期15卷 1141页

作者： Magsi, Amjad Hussain Diez, Luis Enrique Knauth, Stefan Univ Deusto Fac Engn Avda Univ 24 Bilbao 48007 Spain Hsch Tech Stuttgart Fac Comp Sci Geomat & Math Schellingstr 24 D-70174 Stuttgart Germany

The availability of raw Global Navigation Satellites System (GNSS) measurements in Android smartphones fosters advancements in high-precision positioning for mass-market devices. However, challenges like inconsistent pseudo-range and carrier phase observations, limited dual-frequency data integrity, and unidentified hardware biases on the receiver side prevent the ambiguity resolution of smartphone GNSS. Consequently, relying solely on GNSS for high-precision positioning may result in frequent cycle slips in complex conditions such as deep urban canyons, underpasses, forests, and indoor areas due to non-line-of-sight (NLOS) and multipath conditions. Inertial/GNSS fusion is the traditional common solution to tackle these challenges because of their complementary capabilities. For pedestrians and smartphones with low-cost inertial sensors, the usual architecture is Pedestrian Dead Reckoning (PDR)+ GNSS. In addition to this, different GNSS processing techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) have also been integrated with INS. However, integration with PDR has been limited and only with Kalman Filter (KF) and its variants being the main fusion techniques. Recently, factor graph optimization (FGO) has started to be used as a fusion technique due to its superior accuracy. To the best of our knowledge, on the one hand, no work has tested the fusion of GNSS Post-Processed Kinematics (PPK) and PDR on smartphones. And, on the other hand, the works that have evaluated the fusion of GNSS and PDR employing FGO have always performed it using the GNSS Single-Point Positioning (SPP) technique. Therefore, this work aims to combine the use of the GNSS PPK technique and the FGO fusion technique to evaluate the improvement in accuracy that can be obtained on a smartphone compared with the usual GNSS SPP and KF fusion strategies. We improved the Google Pixel 4 smartphone GNSS using Post-Processed Kinematics (PPK) with the open-source RTKLIB 2.4.3 sof

关键词： factor graph optimization fusion GNSS Kalman Filter PDR post-processing kinematics smartphone

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Sparse Instantiation of Bias Nodes for factor graph-based Terrain-referenced Navigation

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第11期22卷 3364-3376页

作者： Park, Junwoo Bang, Hyochoong Korea Adv Inst Sci & Technol Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea

This study presents a factor graph optimization (FGO)-based integration of the terrain-referenced navigation (TRN) and inertial navigation. Particularly, the problem is subject to multiple sources of bias: 1) measurement bias of the radar altimeter, and 2) odometry bias of the inertial navigation system (INS). Considering relatively slower dynamics of bias propagation compared to the INS, the proposed method instantiates bias nodes more sparsely than position nodes, achieving more stable and accurate results. Due to highly ambiguous characteristics of terrain elevation, the TRN is mostly tackled with non-parametric Bayesian estimators, such as the particle filter (PF). However, the PF has a weakness in solving heterogeneously scaled problem, where in this case the bias sources exhibiting small noise characteristics, and the filtering regime cannot explore correlations among historical measurements. We then demonstrate that fixed-lag smoothing of FGO, which optimizes a windowed bundle of factors, can perform as well as or better than the PF approach via multiple stepwise numerical experiments. Moreover, by sparsely instantiating nodes that designate biases, the system dimension is reduced, thus reducing the average computation time by up to 60% compared to the fully connected case.

关键词： Biased measurement biased odometry factor graph optimization sparse node instantiation terrain-referenced navigation

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Tightly-Coupled LiDAR-IMU-Wheel Odometry With Online Calibration of a Kinematic Model for Skid-Steering Robots

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IEEE ACCESS 2024年 12卷 134728-134738页

作者： Okawara, Taku Koide, Kenji Oishi, Shuji Yokozuka, Masashi Banno, Atsuhiko Uno, Kentaro Yoshida, Kazuya Tohoku Univ Grad Sch Engn Dept Aerosp Engn Space Robot Lab Sendai Miyagi 9808579 Japan Natl Inst Adv Ind Sci & Technol Dept Informat Technol & Human Factors Tsukuba Ibaraki 3058568 Japan

Tunnels and long corridors are challenging environments for LiDAR-based odometry estimation algorithms because a LiDAR point cloud should degenerate (i.e., point cloud matching cannot work properly) in such environments. To tackle point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel odometry algorithm incorporating online calibration of a kinematic model for skid-steering robots. We propose a full linear wheel odometry factor, which not only serves as a motion constraint but also performs the online calibration of kinematic models. Despite the dynamically changing kinematic parameters (e.g., wheel radii changes caused by tire pressures) and terrain conditions, our method can address the model error via online calibration. Moreover, our method enables an accurate localization in cases of degenerated environments, such as long and straight corridors, by calibration while point cloud-based constraints sufficiently operate. Furthermore, we estimate the uncertainty (i.e., covariance matrix) of the wheel odometry online for creating a constraint with a reasonable statistical model even in rough terrains. The proposed method is validated through three experiments. The first indoor experiment shows that the proposed method is robust in severe degeneracy cases (long corridors) and changes in the wheel radii. The second outdoor experiment demonstrates that our method accurately estimates the sensor trajectory despite rough outdoor terrain thanks to online uncertainty estimation of wheel odometry. The third experiment shows the proposed online calibration enables robust odometry estimation in a condition that terrains change.

关键词： Odometry Wheels Robots Mobile robots Calibration Kinematics Point cloud compression State estimation factor graph optimization sensor fusion online calibration point cloud degeneration online uncertainty (covariance matrix) estimation state estimation wheel slippage

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LIWOM-GD: Enhanced LiDAR-Inertial-Wheel Odometry and Mapping by Fusion With Ground Constraint and Dynamic Points Elimination

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IEEE SENSORS JOURNAL 2024年第19期24卷 30287-30303页

作者： Zhang, Chizhou Chen, Mingsong Wang, Guanqiang Lin, Yongcheng Li, Kai Wu, Minjie Li, Zehao Wang, Qiu Cent South Univ Light Alloy Res Inst Changsha 410017 Peoples R China Cent South Univ Sch Mech & Elect Engn Changsha 410017 Peoples R China

The mitigation of the drift of the odometer and the ghost trail effect in the map is a challenge for simultaneous localization and mapping (SLAM). Facing the challenge, a novel method, called LIWOM-GD, is proposed via factor graph optimization, which tightly fuses LiDAR, inertial measurement unit (IMU), and wheel encoder measurements. First, the front-end module provides undistorted LiDAR scans, the pose states to be optimized, and the LiDAR-inertial (LI) odometry factor. Second, the point cloud and pre-integration processing module implements ground segmentation, IMU, and encoder pre-integration to form the ground plane, IMU, and encoder factors. Furthermore, an enhanced dynamic point removal method is integrated into the processing module. Third, a typical loop closure detection (LCD) method is used to obtain a loop closure factor. Finally, all the above factors are added to the factor graph to obtain the optimized results based on the GTSAM library. The proposed SLAM system tightly fuses three sensor measurements based on the factor graph, adding global plane constraint and dynamic points removal module based on spatiotemporal normals. The experimental tests are conducted on both benchmark and self-collected datasets. The results demonstrate that our method not only achieves more robust localization and mapping in degraded environments but also effectively implements dynamic point removal compared with the state-of-the-art SLAM methods.

关键词： Laser radar Point cloud compression Odometry Simultaneous localization and mapping Sensors Accuracy Location awareness Degraded environments dynamic point removal factor graph optimization ghost trail effect simultaneous localization and mapping (SLAM)

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A Novel factor graph Framework for Tightly Coupled GNSS/INS Integration With Carrier-Phase Ambiguity Resolution

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13091-13105页

作者： Shen, Zhiheng Li, Xingxing Wang, Xuanbin Wu, Zongzhou Li, Xin Zhou, Yuxuan Li, Shengyu Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

Accurate position, velocity and orientation are essential for the autonomous navigation of unmanned vehicles. The integration of GNSS and INS that can deliver continuous navigation states is widely used for intelligent vehicle systems. In this paper, we propose a tightly coupled GNSS/INS positioning framework with carrier-phase ambiguity resolution based on factor graph optimization (FGO). In this approach, a sliding window optimizer is employed to fuse the multi-GNSS pseudorange and carrier-phase observations with inertial measurements. The same ambiguity within the window is considered as a state node, and the constraint on the ambiguity is continuously preserved by marginalization. To further improve the accuracy and reliability of precise positioning, the carrier-phase ambiguity resolution is introduced to the FGO-based GNSS/INS framework. When the vehicle is detected to be stationary, a zero-velocity constraint and an attitude invariant constraint will be imposed. Several experimental results indicate that the proposed method can accomplish the centimeter-level position estimation performance with beyond 90% positioning availability (horizontal $<$ 10 cm and vertical $<$ 10 cm) and outperforms the current state-of-the-art filter-based tightly coupled method.

关键词： Global navigation satellite system optimization Satellites Position measurement Extraterrestrial measurements Vectors Estimation Tightly coupled integration precise positioning factor graph optimization integer ambiguity resolution

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