A Robust and Fast Method to the Perspective-<i>n</i>-Point Problem for Camera Pose Estimation

Zhuang, Shengbin; Zhao, Zongmin; Lin, Chih-Lung; Wang, Dongfeng; Fu, Chong; Du, Kangning

doi:10.1109/jsen.2023.3266392

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…This study focuses on the orientation between the camera and world coordinates. To obtain rotations and translations for online camera calibration, targets such as registered objects [18,19], road marker lanes [20], and objects with apparent appearances [21] are commonly used. However, these methods cannot cover the case regarding varying extrinsic parameters.…”

Section: Related Workmentioning

confidence: 99%

Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single Vanishing Point from Road Lane

Li,

Kim,

Kakani

et al. 2024

Sensors

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This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including avgE, minE, maxE, ssE, and Stdev, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This potency of the method is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications.

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Section: Related Workmentioning

confidence: 99%

Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single Vanishing Point from Road Lane

Li,

Kim,

Kakani

et al. 2024

Sensors

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show abstract

“…Considering factors such as on-board environment and cost, a low-power, lightweight, and cost-effective monocular sensor is more suitable. Similarly, monocular sensors are used in other pose estimation tasks, such as 2D hand pose estimation [19], camera pose estimation [20], head pose estimation [21], etc.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer

Ye,

Ren,

Zhu

et al. 2023

Remote Sensing

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Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods.

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PnP-UGCSuperGlue: deep learning drone image matching algorithm for visual localization

Guo,

Yang,

et al. 2024

J Supercomput

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A Robust and Fast Method to the Perspective-n-Point Problem for Camera Pose Estimation

Cited by 5 publications

References 26 publications

Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single Vanishing Point from Road Lane

Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single Vanishing Point from Road Lane

An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer

PnP-UGCSuperGlue: deep learning drone image matching algorithm for visual localization

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