Pose Estimation of a Six Degrees of Freedom Pipe-Bender using a 3D-Visual Measurement System of High Accuracy

PurposeThe purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling (AAR) for unmanned aerial vehicle, which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.Design/methodology/approachMethods and techniques for marker detection, feature matching and pose estimation have been designed and implemented in the visual measurement system.FindingsThe simple blob detection (SBD) method is adopted, which outperforms the Laplacian of Gaussian method. And a novel noise-elimination algorithm is proposed for excluding the noise points. Besides, a novel feature matching algorithm based on perspective transformation is proposed. Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.Practical implicationsThe visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.Originality/valueThe SBD method is used to detect the features and a novel noise-elimination algorithm is proposed. Besides, a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

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Compensation of geometric and elastic errors in large manipulators with an application to a high accuracy medical system

Robotica ◽

10.1017/s0263574702004058 ◽

2002 ◽

Vol 20 (3) ◽

pp. 341-352 ◽

Cited By ~ 24

Author(s):

Ph. Drouet ◽

S. Dubowsky ◽

S. Zeghloul ◽

C. Mavroidis

Keyword(s):

Degrees Of Freedom ◽

High Accuracy ◽

Medical Robot ◽

Serial Manipulators ◽

High Position ◽

Six Degrees Of Freedom ◽

Position Accuracy ◽

End Point ◽

Compensation Process ◽

Very High

A method is presented that compensates for manipulator end-point errors in order to achieve very high position accuracy. The measured end-point error is decomposed into generalized geometric and elastic error parameters that are used in an analytical model to calibrate the system as a function of its configuration and the task loads, including any payload weight. The method exploits the fundamental mechanics of serial manipulators to yield a non-iterative compensation process that only requires the identification of parameters that are function only of one variable. The resulting method is computationally simple and requires far less measured data than might be expected. The method is applied to a six degrees-of-freedom (DOF) medical robot that positions patients for cancer proton therapy to enable it to achieve very high accuracy. Experimental results show the effectiveness of the method.

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Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation

Applied Sciences ◽

10.3390/app10165442 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5442

Author(s):

Ryo Hachiuma ◽

Hideo Saito

Keyword(s):

Pose Estimation ◽

Degrees Of Freedom ◽

Shape Representation ◽

Estimation Method ◽

Depth Image ◽

Six Degrees Of Freedom ◽

Depth Images ◽

Object Pose Estimation ◽

Primitive Shape ◽

Conventional Methods

This paper presents a method for estimating the six Degrees of Freedom (6DoF) pose of texture-less primitive-shaped objects from depth images. As the conventional methods for object pose estimation require rich texture or geometric features to the target objects, these methods are not suitable for texture-less and geometrically simple shaped objects. In order to estimate the pose of the primitive-shaped object, the parameters that represent primitive shapes are estimated. However, these methods explicitly limit the number of types of primitive shapes that can be estimated. We employ superquadrics as a primitive shape representation that can represent various types of primitive shapes with only a few parameters. In order to estimate the superquadric parameters of primitive-shaped objects, the point cloud of the object must be segmented from a depth image. It is known that the parameter estimation is sensitive to outliers, which are caused by the miss-segmentation of the depth image. Therefore, we propose a novel estimation method for superquadric parameters that are robust to outliers. In the experiment, we constructed a dataset in which the person grasps and moves the primitive-shaped objects. The experimental results show that our estimation method outperformed three conventional methods and the baseline method.

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Can we Localize an AV from a Single Image? Deep-Geometric 6 DoF Localization in Topo-metric Maps

Journal of Autonomous Vehicles and Systems ◽

10.1115/1.4052604 ◽

2021 ◽

pp. 1-13

Author(s):

Punarjay Chakravarty ◽

Tom Roussel ◽

Gaurav Pandey ◽

Tinne Tuytelaars

Keyword(s):

Pose Estimation ◽

Autonomous Vehicles ◽

Hybrid Algorithm ◽

Degrees Of Freedom ◽

Single Camera ◽

Single Image ◽

Six Degrees Of Freedom ◽

Localization Algorithms ◽

Mapping Process ◽

Set Of Points

Abstract We describe a Deep-Geometric Localizer that is able to estimate the full six degrees-of-freedom (DoF) global pose of the camera from a single image in a previously mapped environment. Our map is a topo-metric one, with discrete topological nodes whose 6DOF poses are known. Each topo-node in our map also comprises of a set of points, whose 2D features and 3D locations are stored as part of the mapping process. For the mapping phase, we utilise a stereo camera and a regular stereo visual SLAM pipeline. During the localization phase, we take a single camera image, localize it to a topological node using Deep Learning, and use a geometric algorithm (PnP) on the matched 2D features (and their 3D positions in the topo map) to determine the full 6DOF globally consistent pose of the camera. Our method divorces the mapping and the localization algorithms and sensors (stereo and mono), and allows accurate 6DOF pose estimation in a previously mapped environment using a single camera. With results in simulated and real environments, our hybrid algorithm is particularly useful for autonomous vehicles (AVs) and shuttles that might repeatedly traverse the same route.

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The Wide-Open Three-Legged Parallel Robot for Long-Bone Fracture Reduction

Journal of Mechanisms and Robotics ◽

10.1115/1.4035495 ◽

2017 ◽

Vol 9 (1) ◽

Cited By ~ 7

Author(s):

Mohammad H. Abedinnasab ◽

Farzam Farahmand ◽

Jaime Gallardo-Alvarado

Keyword(s):

Bone Fracture ◽

Degrees Of Freedom ◽

Experimental Tests ◽

Parallel Robot ◽

High Accuracy ◽

Fracture Reduction ◽

Long Bone ◽

Long Bones ◽

Six Degrees Of Freedom ◽

Long Bone Fracture

Robotic reduction of long bones is associated with the need for considerable force and high precision. To balance the accuracy, payload, and workspace, we have designed a new six degrees-of-freedom three-legged wide-open robotic system for long-bone fracture reduction. Thanks to the low number of legs and their nonsymmetrical configuration, the mechanism enjoys a unique architecture with a frontally open half-plane. This facilitates positioning the leg inside the mechanism and provides a large workspace for surgical maneuvers, as shown and compared to the well-known Gough–Stewart platform. The experimental tests on a phantom reveal that the mechanism is well capable of applying the desired reduction steps against the large muscular payloads with high accuracy.

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Automatic Calibration of a Six-Degrees-of-Freedom Pose Estimation System

IEEE Sensors Journal ◽

10.1109/jsen.2019.2921644 ◽

2019 ◽

Vol 19 (19) ◽

pp. 8824-8831 ◽

Cited By ~ 2

Author(s):

Wouter Jansen ◽

Dennis Laurijssen ◽

Walter Daems ◽

Jan Steckel

Keyword(s):

Pose Estimation ◽

Degrees Of Freedom ◽

Automatic Calibration ◽

Six Degrees Of Freedom ◽

Estimation System

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Vision Based 3D Tracking and Pose Estimation for Mixed Reality

Emerging Technologies of Augmented Reality ◽

10.4018/978-1-59904-066-0.ch001 ◽

2011 ◽

pp. 1-22 ◽

Cited By ~ 3

Author(s):

Pascal Fua ◽

Vincent Lepetit

Keyword(s):

Pose Estimation ◽

Degrees Of Freedom ◽

Mixed Reality ◽

Low Cost ◽

3D Tracking ◽

Robust Algorithms ◽

Six Degrees Of Freedom ◽

Non Invasive ◽

Camera Position ◽

Position And Orientation

Mixed Reality applications require accurate knowledge of the relative positions of the camera and the scene. When either of them moves, this means keeping track in real-time of all six degrees of freedom that define the camera position and orientation relative to the scene, or, equivalently, the 3D displacement of an object relative to the camera. Many technologies have been tried to achieve this goal. However, Computer Vision is the only one that has the potential to yield non-invasive, accurate and low-cost solutions to this problem, provided that one is willing to invest the effort required to develop sufficiently robust algorithms. In this chapter, we therefore discuss some of the most promising approaches, their strengths, and their weaknesses.

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Pose Estimation of Cooperative Targets for Space Exploration Task Based on Binocular Visual Measurement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.268 ◽

2013 ◽

Vol 333-335 ◽

pp. 268-274

Author(s):

Jing Jing Wang ◽

Jian Yu Huang ◽

Shi Yin Qin

Keyword(s):

Pose Estimation ◽

Three Dimensional ◽

Least Square Method ◽

Estimation Algorithm ◽

High Accuracy ◽

Least Square ◽

Estimation Accuracy ◽

Feature Points ◽

Well Performance ◽

Visual Measurement

In this paper, a high accuracy and efficiency pose estimation algorithm is proposed for space cooperative targets in RVD based on binocular visual measurement. At first, the scheme of visual measurement toward RVD is presented and the environment conditions and performance requirement are analysed and discussed. Then the relationship of pose estimation with detection and tracking is studied to give an implementing strategy of pose estimation with high accuracy and efficiency. Moreover, the key point is focused on the pose estimation of cooperative targets, in which a stereo vision mapping relation between three dimensionl coordinates of spacial feature points of cooperative targets and their corresponding image coordinates is established, then the least square method is employed to estimate the three-dimensional coordinates of feature points so as to calculate the relative position and attitude between tracking spacecraft and target spacecraft with high precision, finally a series of experimental resluts indicate that the proposed pose estimation algorithm under binocular visual measurement demonstrates well performance in the estimation accuracy, anti-noise and real-time thus can achieve the application requriements of RVD under binocular visual measurement.

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