Fast and reliable minimal relative pose estimation under planar motion

Choi, Sunglok; Kim, Jong-Hwan

doi:10.1016/j.imavis.2017.08.007

Cited by 31 publications

(72 citation statements)

References 10 publications

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“…To further improve the computational efficiency and reliability of relative pose estimation, assumptions about the camera motion or additional information can help to reduce the number of required point correspondences across views. For example, if the camera is mounted on ground robots and follows planar motion, the relative pose of two views has only 2DOF and can be estimated by using 2 point correspondences [31,8,9]. By taking into account the Ackermann motion model, only 1 point correspondence is sufficient to recover the camera motion [35].…”

Section: Related Workmentioning

confidence: 99%

“…Eqs. (4), (8) and (9) together with the implicit constraints of the trigonometric functions can be reformulated as:…”

Section: Least-squares Solutionmentioning

confidence: 99%

“…The coefficients a i , b i , c i and d i denote the problem coefficients in Eqs. (4), (8) and (9). This equation system has 4 unknowns and 5 independent constraints, thus it is overconstrained.…”

Section: Least-squares Solutionmentioning

confidence: 99%

“…By exploiting motion constraints on camera movements or utilizing an additional sensor like an inertial measurement unit (IMU), the minimal number of point correspondences needed can be further reduced, which makes the outlier removal more efficient and numerically more stable. For instance, two points are sufficient to recover camera motion under the planar motion assumption since the pose only has two degrees of freedom (DOF) [31,8,9]. Another example is to make use of the Ackermann steering principle which allows us to parameterize the camera motion with only one point correspondence [35,21].…”

Section: Introductionmentioning

confidence: 99%

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Minimal Solutions for Relative Pose With a Single Affine Correspondence

Guan

Zhao

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closedform solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations.

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Section: Related Workmentioning

confidence: 99%

“…Eqs. (4), (8) and (9) together with the implicit constraints of the trigonometric functions can be reformulated as:…”

Section: Least-squares Solutionmentioning

confidence: 99%

Section: Least-squares Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Minimal Solutions for Relative Pose With a Single Affine Correspondence

Guan

Zhao

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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show abstract

“…Scaramuzza [34] models the 2D motion to approximate planar circular motion, thus a 1-point minimum relative pose estimate solver can be applied. With a general planer motion model, an iterative 2-point algorithm [35] using the Newton method, a 3-point algorithm which is using a linear equation and non-iterative 2-point algorithms [36] were proposed.…”

Section: Related Workmentioning

confidence: 99%

RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots

Shan

Schwertfeger

2019

Sensors

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Using camera sensors for ground robot Simultaneous Localization and Mapping (SLAM) has many benefits over laser-based approaches, such as the low cost and higher robustness. RGBD sensors promise the best of both worlds: dense data from cameras with depth information. This paper proposes to fuse RGBD and IMU data for a visual SLAM system, called VINS-RGBD, that is built upon the open source VINS-Mono software. The paper analyses the VINS approach and highlights the observability problems. Then, we extend the VINS-Mono system to make use of the depth data during the initialization process as well as during the VIO (Visual Inertial Odometry) phase. Furthermore, we integrate a mapping system based on subsampled depth data and octree filtering to achieve real-time mapping, including loop closing. We provide the software as well as datasets for evaluation. Our extensive experiments are performed with hand-held, wheeled and tracked robots in different environments. We show that ORB-SLAM2 fails for our application and see that our VINS-RGBD approach is superior to VINS-Mono.

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Relative Pose Estimation of Calibrated Cameras with Known $$\mathrm {SE}(3)$$ Invariants

Martyushev

Lee

2020

Lecture Notes in Computer Science

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Fast and reliable minimal relative pose estimation under planar motion

Cited by 31 publications

References 10 publications

Minimal Solutions for Relative Pose With a Single Affine Correspondence

Minimal Solutions for Relative Pose With a Single Affine Correspondence

RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots

Relative Pose Estimation of Calibrated Cameras with Known $$\mathrm {SE}(3)$$ Invariants

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