Rigid body pose estimation accuracy: a comparison of single-camera and stereo techniques

1994 ◽  
Author(s):  
Hassan Mostafavi
Keyword(s):  
Rigid Body ◽  
Pose Estimation ◽  
Estimation Accuracy ◽  
Single Camera ◽  
Body Pose Estimation

Using an Extended Kalman Filter for Rigid Body Pose Estimation

2004 ◽  
Vol 127 (3) ◽  
pp. 475-483 ◽  
Author(s):  
Kjartan Halvorsen ◽  
Torsten Söderström ◽  
Virgil Stokes ◽  
Håkan Lanshammar
Keyword(s):  
Kalman Filter ◽  
Rigid Body ◽  
State Space ◽  
Pose Estimation ◽  
Measurement Errors ◽  
The State ◽  
State Space Models ◽  
Marker Data ◽  
Measurement Function ◽  
Body Pose Estimation

Rigid body pose is commonly represented as the rigid body transformation from one (often reference) pose to another. This is usually computed for each frame of data without any assumptions or restrictions on the temporal change of the pose. The most common algorithm was proposed by Söderkvist and Wedin (1993, “Determining the Movements of the Skeleton Using Well-configured Markers,” J. Biomech., 26, pp. 1473–1477), and implies the assumption that measurement errors are isotropic and homogenous. This paper describes an alternative method based on a state space formulation and the application of an extended Kalman filter (EKF). State space models are formulated, which describe the kinematics of the rigid body. The state vector consists of six generalized coordinates (corresponding to the 6 degrees of freedom), and their first time derivatives. The state space models have linear dynamics, while the measurement function is a nonlinear relation between the state vector and the observations (marker positions). An analytical expression for the linearized measurement function is derived. Tracking the rigid body motion using an EKF enables the use of a priori information on the measurement noise and type of motion to tune the filter. The EKF is time variant, which allows for a natural way of handling temporarily missing marker data. State updates are based on all the information available at each time step, even when data from fewer than three markers are available. Comparison with the method of Söderkvist and Wedin on simulated data showed a considerable improvement in accuracy with the proposed EKF method when marker data was temporarily missing. The proposed method offers an improvement in accuracy of rigid body pose estimation by incorporating knowledge of the characteristics of the movement and the measurement errors. Analytical expressions for the linearized system equations are provided, which eliminate the need for approximate discrete differentiation and which facilitate a fast implementation.

scholarly journals Depth Estimation of a Deformable Object via a Monocular Camera

2019 ◽  
Vol 9 (7) ◽  
pp. 1366 ◽  
Author(s):  
Guolai Jiang ◽  
Shaokun Jin ◽  
Yongsheng Ou ◽  
Shoujun Zhou
Keyword(s):  
Pose Estimation ◽  
A Priori ◽  
Depth Estimation ◽  
Target Object ◽  
Estimation Accuracy ◽  
Single Camera ◽  
Deformable Object ◽  
Nao Robot ◽  
Feature Based ◽  
Monocular Image

The depth estimation of the 3D deformable object has become increasingly crucial to various intelligent applications. In this paper, we propose a feature-based approach for accurate depth estimation of a deformable 3D object with a single camera, which reduces the problem of depth estimation to a pose estimation problem. The proposed method needs to reconstruct the target object at the very beginning. With the 3D reconstruction as an a priori model, only one monocular image is required afterwards to estimate the target object’s depth accurately, regardless of pose changes or deformability of the object. Experiments are taken on an NAO robot and a human to evaluate the depth estimation accuracy by the proposed method.

scholarly journals Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment

2020 ◽  
Vol 11 (1) ◽  
pp. 3
Author(s):  
Laura Gonçalves Ribeiro ◽  
Olli J. Suominen ◽  
Ahmed Durmush ◽  
Sari Peltonen ◽  
Emilio Ruiz Morales ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Stereo Matching ◽  
Motion Parallax ◽  
Extreme Temperature ◽  
Single Camera ◽  
Safety Critical ◽  
Depth Cues ◽  
Significant Challenge ◽  
Visual Technologies ◽  
Marker Detection

Visual technologies have an indispensable role in safety-critical applications, where tasks must often be performed through teleoperation. Due to the lack of stereoscopic and motion parallax depth cues in conventional images, alignment tasks pose a significant challenge to remote operation. In this context, machine vision can provide mission-critical information to augment the operator’s perception. In this paper, we propose a retro-reflector marker-based teleoperation aid to be used in hostile remote handling environments. The system computes the remote manipulator’s position with respect to the target using a set of one or two low-resolution cameras attached to its wrist. We develop an end-to-end pipeline of calibration, marker detection, and pose estimation, and extensively study the performance of the overall system. The results demonstrate that we have successfully engineered a retro-reflective marker from materials that can withstand the extreme temperature and radiation levels of the environment. Furthermore, we demonstrate that the proposed maker-based approach provides robust and reliable estimates and significantly outperforms a previous stereo-matching-based approach, even with a single camera.

WTLS-Bayes Filter-based Relative Pose Estimation for Space Non-cooperative Unknown Target Without a Priori Knowledge

2021 ◽  
Author(s):  
CHENGGUANG ZHU ◽  
zhongpai Gao ◽  
Jiankang Zhao ◽  
Haihui Long ◽  
Chuanqi Liu
Keyword(s):  
Pose Estimation ◽  
A Priori ◽  
Convergence Time ◽  
Estimation Accuracy ◽  
A Priori Knowledge ◽  
One Step ◽  
Relative Pose ◽  
The One ◽  
Negative Factors ◽  
Priori Knowledge

Abstract The relative pose estimation of a space noncooperative target is an attractive yet challenging task due to the complexity of the target background and illumination, and the lack of a priori knowledge. Unfortunately, these negative factors have a grave impact on the estimation accuracy and the robustness of filter algorithms. In response, this paper proposes a novel filter algorithm to estimate the relative pose to improve the robustness based on a stereovision system. First, to obtain a coarse relative pose, the weighted total least squares (WTLS) algorithm is adopted to estimate the relative pose based on several feature points. The resulting relative pose is fed into the subsequent filter scheme as observation quantities. Second, the classic Bayes filter is exploited to estimate the relative state except for moment-of-inertia ratios. Additionally, the one-step prediction results are used as feedback for WTLS initialization. The proposed algorithm successfully eliminates the dependency on continuous tracking of several fixed points. Finally, comparison experiments demonstrate that the proposed algorithm presents a better performance in terms of robustness and convergence time.

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