Factor graph based navigation and positioning for control system design: A review

Wu, Xiangqing; Xiao, Bing; Wu, Cihang; Guo, Yiming; Li, Lingwei

doi:10.1016/j.cja.2021.09.001

Cited by 34 publications

(14 citation statements)

References 61 publications

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“…Measurement noises are also considered and addressed to extend the application scopes of the method. As some of the future work, actuator constraints like input saturation and the factor graph-based navigation and positioning (Wu et al, 2021b) for measurement can be considered.…”

Section: Discussionmentioning

confidence: 99%

Predefined-time sliding manifold-based fixed-time attitude stabilization control of receiver aircraft with measurement noises

Yan

et al. 2022

Transactions of the Institute of Measurement and Control

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This paper investigates the challenging attitude stabilization control problem of receiver aircraft subject to uncertain and time-varying inertia, and wind disturbances in the aerial refueling task. An adaptive fixed-time control strategy is proposed. In this approach, a predefined-time sliding manifold is preliminarily developed. The main feature is that the settling time is not only independent of initial conditions but also can be simply and explicitly defined in advance through a single parameter in the sliding mode phase. On this basis, an adaptive control law is developed to guarantee that the trajectory of attitude can be stabilized into a small region containing the origin. The upper bound of unknown disturbance is not required to implement the proposed controller, which relaxes the restrictions of design for control systems. Moreover, the measuring noises are explicitly considered and addressed to make this approach still available in the noisy measurement environment. The Lyapunov stability analysis and numerical simulations verify the effectiveness of the proposed control strategy.

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Section: Discussionmentioning

confidence: 99%

Predefined-time sliding manifold-based fixed-time attitude stabilization control of receiver aircraft with measurement noises

Yan

et al. 2022

Transactions of the Institute of Measurement and Control

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“…The taxonomy of sensor fusion methods for mobile robot odometry is dependent on their working principle, and adopted from recent surveys on sensor fusion [ 10 , 248 , 251 , 252 ]. Filter-based methods [ 253 ] and optimization-based methods [ 254 , 255 ] are summarized below.…”

Section: Sensor Fusionmentioning

confidence: 99%

“…This is the so-called full-SLAM solution. An intuitive way to achieve this is via the factor graph method, which builds a graph whose vertices encode robot poses and feature locations, with edges encoding the constraints between vertices arising from measurements [ 255 , 287 ]. This is cast into an optimization problem that minimizes

where

stands for the set index pair between nodes,

stands for the information matrix between nodes

and

, and

is the error function modelling the error between expected and measured spatial constraint.…”

Section: Sensor Fusionmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

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“…Compared with the traditional optimization algorithm, the factor graph optimization adopts the incremental reasoning algorithm, which has strong real-time performance [ 26 , 27 ]. The FGO can better solve the nonlinear problem of some state equations and observation equations in the navigation system, which lays the foundation for the realization of high-precision and robust positioning and navigation technology [ 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes

Dai

Liu

Hao

et al. 2022

Sensors

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With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations.

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Factor graph based navigation and positioning for control system design: A review

Cited by 34 publications

References 61 publications

Predefined-time sliding manifold-based fixed-time attitude stabilization control of receiver aircraft with measurement noises

Predefined-time sliding manifold-based fixed-time attitude stabilization control of receiver aircraft with measurement noises

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes

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