Globally stable velocity estimation using normalized velocity measurement

Bjørne, Elias; Brekke, Edmund; Bryne, Torleiv H.; Delaune, Jeff; Johansen, Tor Arne

doi:10.1177/0278364919887436

Cited by 3 publications

(3 citation statements)

References 35 publications

(96 reference statements)

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“…Hence, it overcomes the issue of inconsistency and inaccuracy from bad initial guess, and enjoys low computational complexity with linear growth with respect to the number of features. C3 Relaxing significantly the PE or UCO condition required in some recent results, e.g., [5,21,32], and only requiring an extremely weak assumption, i.e. interval excitation (IE).…”

Section: Contributionsmentioning

confidence: 99%

“…It is well known that UCO of LTV systems is equivalent to PE of some intermediate variables [29]. Besides, the PE and UCO conditions are also indispensable in some related problems, e.g., locolization using range or direction measurements [11], velocity estimation using normalized measurement [5] and feature depth observation [8]. Theoretically, the absence of UCO or PE is yet another source of inaccuracy and inconsistency-apart from nonlinearity and non-convexity as illustrated above-in filtering approaches.…”

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confidence: 99%

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An almost globally convergent observer for visual SLAM without persistent excitation

Jin

Wang

Shi

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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This paper introduces a new linear parameterization to the problem of visual inertial simultaneous localization and mapping (VI-SLAM)-without anyapproximation-for the case only using information from a single monocular camera and an inertial measurement unit. In this problem set, the system state evolves on the nonlinear manifold SE(3)×R 3n , on which we design dynamic extensions carefully to generate invariant foliations, such that the problem can be reformulated into online constant parameter identification, then interestingly with linear regression models obtained. It demonstrates that VI-SLAM can be translated into a linear least squares problem, in the deterministic sense, globally and exactly. Based on this observation, we propose a novel SLAM observer, following the recently established parameter estimation-based observer (PEBO) methodology. A notable merit is that the proposed observer enjoys almost global asymptotic stability, requiring neither persistency of excitation nor uniform complete observability, which, however, are widely adopted in most existing works with provable stability but can hardly be assured in many practical scenarios.

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Section: Contributionsmentioning

confidence: 99%

mentioning

confidence: 99%

An almost globally convergent observer for visual SLAM without persistent excitation

Jin

Wang

Shi

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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“…The spatial projection of image pixel points using multiview geometry as well as feature point matching and optical flow tracking is used to obtain a spatial point cloud, and the matching of this point cloud map with the current image and the PnP algorithm is used to localize the robot system [ 11 ]. Its localization optimization is performed with the reprojection error or image error as the optimization term for the pose solution.…”

Section: Status Of Researchmentioning

confidence: 99%

Predictive Control‐Based Completeness Analysis and Global Calibration of Robot Vision Features

Lou

2021

Computational Intelligence and Neuroscience

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This paper provides an in-depth study and analysis of robot vision features for predictive control and a global calibration of their feature completeness. The acquisition and use of the complete macrofeature set are studied in the context of a robot task by defining the complete macrofeature set at the level of the overall purpose and constraints of the robot vision servo task. The visual feature set that can fully characterize the macropurpose and constraints of a vision servo task is defined as the complete macrofeature set. Due to the complexity of the task, a part of the features of the complete macrofeature set is obtained directly from the image, and another part of the features is obtained from the image by inference. The task is guaranteed to be completely based on a robust calibration-free visual serving strategy based on interference observer that is proposed to complete the visual serving task with high performance. To address the problems of singular values, local minima, and insufficient robustness in the traditional scale-free vision servo algorithm, a new scale-free vision servo method is proposed to construct a dual closed-loop vision servo structure based on interference observer, which ensures the closed-loop stability of the system through the Q-filter-based interference observer, while estimating and eliminating the interference consisting of hand-eye mapping model uncertainty and controlled robot input interference. The equivalent interference consisting of hand-eye mapping model uncertainty, controlled robot input interference, and detection noise is estimated and eliminated to obtain an inner-loop structure that presents a nominal model externally, and then an outer-loop controller is designed according to the nominal model to achieve the best performance of the system dynamic performance and robustness to optimally perform the vision servo task.

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Absolute velocity estimation of UAVs based on phase correlation and monocular vision in unknown GNSS‐denied environments

Deng,

Li,

Shen

et al. 2024

IET Image Processing

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This paper proposes a novel approach for absolute velocity estimation of unmanned aerial vehicles in unknown and unmapped GNSS‐denied environments. The proposed method leverages the advantages of Fourier‐based image phase correlation and utilizes off‐the‐shelf onboard sensors, including a downward‐facing monocular camera, an inertial sensor, and a sonar. The non‐matching tracking approach is particularly appealing, offering accurate estimation while remaining robust against frequency‐dependent noise, significant intensity variations, and time‐varying illumination disturbances. In the proposed method, the first step involves computing global pixel motion from consecutive images using phase correlation, which utilizes the shift property of the Fourier transform. This pixel motion estimation serves as the basis for creating a closed‐loop solution for absolute velocity estimation. To further enhance accuracy, a Kalman filter is implemented to fuse all available data and provide a reliable velocity estimate. Validation of the proposed visual‐inertial technique is conducted through simulation experiments using AirSim and real‐world flight tests. The results demonstrate the practicality and effectiveness of the approach across a range of challenging scenarios.

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Globally stable velocity estimation using normalized velocity measurement

Cited by 3 publications

References 35 publications

An almost globally convergent observer for visual SLAM without persistent excitation

An almost globally convergent observer for visual SLAM without persistent excitation

Predictive Control‐Based Completeness Analysis and Global Calibration of Robot Vision Features

Absolute velocity estimation of UAVs based on phase correlation and monocular vision in unknown GNSS‐denied environments

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