GSPnP: simple and geometric solution for PnP problem

Adli, Sahand Eivazi; Shoaran, Maryam; Noorani, S. Mohammadreza Sayyed

doi:10.1007/s00371-019-01747-x

Cited by 2 publications

(2 citation statements)

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“…, 2021), Ceres-3D-2D solving algorithm (Pfeifer et al. , 2021) and OpenCV-3D-2D solving algorithm (Eivazi Adli et al. , 2020).…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…The resulting T cl matrix is as follows: To better demonstrate the advantages of using the Ceres nonlinear optimization algorithm for extrinsic calibration, three additional comparative methods were applied to the collected data. These methods include the SVD-3D-3D solving algorithm (Su et al, 2021), Ceres-3D-2D solving algorithm (Pfeifer et al, 2021) and OpenCV-3D-2D solving algorithm (Eivazi Adli et al, 2020). With the assurance of identical input data acquisition, default hyper-parameter settings were used across the algorithmic functions.…”

Section: Thermal Imaging Camera and Lidar Extrinsic Calibration Exper...mentioning

confidence: 99%

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Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

Zhang,

Yuan,

Meng

et al. 2024

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Purpose In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments. Design/methodology/approach Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments. Findings The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors. Originality/value This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

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“…, 2021), Ceres-3D-2D solving algorithm (Pfeifer et al. , 2021) and OpenCV-3D-2D solving algorithm (Eivazi Adli et al. , 2020).…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Thermal Imaging Camera and Lidar Extrinsic Calibration Exper...mentioning

confidence: 99%

Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

Zhang,

Yuan,

Meng

et al. 2024

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Low-Complexity Hardware Accelerator for Camera Pose Estimation Based on Fiducial Markers

Sufi

2024

Preprint

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GSPnP: simple and geometric solution for PnP problem

Cited by 2 publications

References 20 publications

Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

Low-Complexity Hardware Accelerator for Camera Pose Estimation Based on Fiducial Markers

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