Total Estimation from RGB Video: On-line Camera Self-Calibration, Non-Rigid Shape and Motion

Agudo, Antonio

doi:10.1109/icpr48806.2021.9412923

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…For example, Faugeras et al (1992) demonstrated camera self-calibration without a pattern, based on which Qian and Chellappa (2004) and Civera et al (2009) proposed to use sum of Gaussian filters. Recently, Agudo (2021) extended the above works to use RGB video with objects of non-rigid shape for camera self-calibration.…”

Section: Camera Calibrationmentioning

confidence: 99%

Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy

Yang¹,

Geneva²,

Zuo³

et al. 2022

Preprint

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In this paper, we study in-depth the problem of online self-calibration for robust and accurate visual-inertial state estimation. In particular, we first perform a complete observability analysis for visual-inertial navigation systems (VINS) with full calibration of sensing parameters, including IMU and camera intrinsics and IMU-camera spatial-temporal extrinsic calibration, along with readout time of rolling shutter (RS) cameras (if used). We investigate different inertial model variants containing IMU intrinsic parameters that encompass most commonly used models for low-cost inertial sensors. With these models, the state transition matrix and visual measurement Jacobians are analytically derived and the observability analysis of linearized VINS with full sensor calibration is performed. The analysis results prove that, as intuitively assumed in the literature, VINS with full sensor calibration has four unobservable directions, corresponding to the system's global yaw and translation, while all sensor calibration parameters are observable given fully-excited 6-axis motion. Moreover, we, for the first time, identify primitive degenerate motions for IMU and camera intrinsic calibration, which, when combined, may produce complex degenerate motions. This result holds true for the different inertial model variants investigated in this work and has significant impacts on practical applications of online self-calibration to many robotic platforms. Extensive Monte-Carlo simulations and real-world experiments are performed to validate both the observability analysis and identified degenerate motions, showing that online self-calibration improves system accuracy and robustness to calibration inaccuracies. We compare the proposed online self-calibration on commonly-used IMUs against the state-of-art offline calibration toolbox Kalibr, and show that the proposed system achieves better consistency and repeatability. As sensor calibration plays an important role in making real VINS work well in practice, based on our analysis and experimental evaluations, we also provide practical guidelines for how to perform online IMU-camera sensor self-calibration.

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Section: Camera Calibrationmentioning

confidence: 99%

Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy

Yang¹,

Geneva²,

Zuo³

et al. 2022

Preprint

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show abstract

“…To ensure the various capabilities of a mobile robot, visual simultaneous localization and mapping (VSLAM) are considered fundamental issues. In recent decades, VSLAM systems [1][2][3][4][5][6][7] based on visual sensors have attracted increasing attention and been well studied, with a rather satisfactory performance that can facilitate high-level tasks [8][9][10]. Typically, some well-performing VSLAM systems have been developed, such as ORB-SLAM2 [3] and LSD-SLAM [2].…”

Section: Introductionmentioning

confidence: 99%

DIR-SLAM: Dynamic Interference Removal for Real-Time VSLAM in Dynamic Environments

Yang

Zhu

et al. 2023

Mobile Information Systems

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Traditional visual simultaneous localization and mapping (VSLAM) systems mostly rely on the static-world assumption, which limits their applications in real-world scenarios with dynamic objects. When there are dynamic objects in the scene, the localization accuracy of the system decreases seriously. In this paper, in order to minimize the interference of dynamic objects in vision localization, we propose a real-time and robust dynamic interference removal (DIR) method, which is based on both prior knowledge and geometry information. Our approach employs a novel lightweight CNN network to output semantic labels and extends the semantics based on the correlations of descriptors to generate a segmented mask. We design a geometric consistency check module to remove the dynamic interference, which computes the bundle adjustment to predetermine the static keypoints, and then the semantic weighted epipolar constraint is used to identify the dynamic outliers. The proposed method is integrated into the front end of ORB-SLAM2 to filter out the dynamic keypoints which are associated with the known and unknown dynamic objects. We conduct experiments on the public TUM RGB-D dataset, the qualitative and quantitative results prove that the DIR method can improve the performance of the state-of-the-art VSLAM system in dynamic scenarios.

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Laryngeal surface reconstructions from monocular endoscopic videos: a structure from motion pipeline for periodic deformations

Regef,

Talasila,

Wiercigroch

et al. 2024

Int J CARS

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Total Estimation from RGB Video: On-line Camera Self-Calibration, Non-Rigid Shape and Motion

Cited by 6 publications

References 40 publications

Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy

Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy

DIR-SLAM: Dynamic Interference Removal for Real-Time VSLAM in Dynamic Environments

Laryngeal surface reconstructions from monocular endoscopic videos: a structure from motion pipeline for periodic deformations

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