Complete solution classification for the perspective-three-point problem

Gao, Xiao-Shan; Hou, Xiaorong; Tang, Jian; Cheng, Hang-Fei

doi:10.1109/tpami.2003.1217599

Cited by 722 publications

(97 citation statements)

References 14 publications

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“…The minimal number of correspondences to solve PnP problem is three. Some researches [11,12] had found that P3P problem with non-collinear points would result in as many as four solutions. Fischler [13] found that P4P problem with non -coplanar points had several solutions.…”

Section: Related Workmentioning

confidence: 99%

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Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Zheng¹,

Zhou²,

Wang³

2014

IJCA

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Section: Related Workmentioning

confidence: 99%

“…In simulation and real experiments, we compared our algorithms with some state-of-the-art and classics algorithms, such as GAO algorithm [12], LHM approach [18], RPP algorithm [19], and EPnP algorithm [20].…”

Section: Simulation Experimentsmentioning

confidence: 99%

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Zheng¹,

Zhou²,

Wang³

2014

IJCA

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“…In general, relative poses of camera and laser system can be estimated in two ways. First way estimates relative poses based on corresponding 3D laser points and homogeneous camera points using the methods in [22], [23]. Other way solves the problem by using corresponding 3D points, in two stages: estimates 3D points of camera based on respective 3D points of LRF, and then estimates extrinsic parameters by using corresponding 3D points.…”

Section: Camera-point Distancementioning

confidence: 99%

Closed-form solution to 3D points for estimating extrinsic parameters of camera and laser sensor

Hoang

Hernández

Park

et al. 2014

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

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Integration systems based on cameras and laser rangefinders has been applied in many researches on robotics, autonomous navigation vehicles, and intelligent transportation systems. Relative information between sensors is usually required for systems using multiple sensors. This paper will present a method for recovering extrinsic parameters of a system based on an omnidirectional camera and laser rangefinder. The calibration method uses a discontiguous calibration pattern, which emphasizes laser information of range. It supports to extract corresponding feature points between camera images and laser range scans. A closed-form solution is also presented for estimating 3D image points based on the PnP technique. Finally, corresponding 3D points are used to recover extrinsic parameters of location and direction of a camera with respect to a laser rangefinder.

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“…The number of beacons in sight during the IS flight are stochastic because many of the same beacons are deployed inside the space station cabin for good coverage. This problem seems to prevent the conventional perspective-N-point method 13,14 from directly solving the pose estimation problem of IS. Pattern recognition methods based on features of the images, such as shape context 15,16 and spectral graph theory 17,18 , are reasonable ways to determine the corresponding relationship between two point sets of different images in most cases.…”

Section: Introductionmentioning

confidence: 99%

A 6-DOF Navigation Method based on Iterative Closest Imaging Point Algorithm

Shi

You

Zhao

et al. 2017

Sci Rep

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To achieve six degree-of-freedom autonomous navigation of an inboard spacecraft, a novel algorithm called iterative closest imaging point (ICIP) is proposed, which deals with the pose estimation problem of a vision navigation system (VNS). This paper introduces the basics of the ICIP algorithm, including mathematical model, algorithm architecture, and convergence theory. On this basis, a navigation method is proposed. This method realizes its initialization using a Gaussian mixture model-based Kalman filter, which simultaneously solves the 3D-to-2D point correspondences and the camera pose. The initial value sensitivity, computational efficiency, robustness, and accuracy of the proposed navigation method are discussed based on simulation results. A navigation experiment verifies that the proposed method works effectively. The three-axis Euler angle accuracy is within 0.19° (1σ), and the three-axis position accuracy is within 1.87 mm (1σ). The ICIP algorithm estimates the full-state pose by merely finding the closest point couples respectively form the images obtained by the VNS and predicted at an initial value. Then the optimized solution of the imaging model is iteratively calculated and the full-state pose is obtained. Benefiting from the absence of a requirement for feature matching, the proposed navigation method offers advantages of low computational complexity, favorable stability, and applicability in an extremely simple environment in comparison with conventional methods.

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Complete solution classification for the perspective-three-point problem

Cited by 722 publications

References 14 publications

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Closed-form solution to 3D points for estimating extrinsic parameters of camera and laser sensor

A 6-DOF Navigation Method based on Iterative Closest Imaging Point Algorithm

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