Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Hardware Platform for a Multi-Robot System : Research and implementation of a multi-robot system with a cooperative behavioral control system

A small demonstration lab with a multi-robot system is being developed at ITK.This thesis considers the development and implementation of a hardware and softwareplatform for this multi-robot system. An overview of the project and systemis presented, followed by a walk-through of the hardware components required andused in this system. A brief overview of the software system is presented. Theproblems within, requirements of and solutions to obstacle detection methods ispresented, followed by a conference paper, written by the author of this thesis, introducingthe Inverted Particle Filter, which is an obstacle detection and mappingmethod developed for resource constrained systems. Previous work on positioningsystems is presented and the positioning system for this multi-robot system isproposed. The overall project and system is discussed and evaluated

Point Cloud Registration for Assembly using Conformal Geometric Algebra

This thesis is a collection of five journal papers and one conference paper. The thesis is on pose alignment and point correspondence estimation for 3D point clouds, and inverse kinematics of industrial robots. The approaches proposed in this thesis are based on conformal geometric algebra, which is an extension of Euclidean geometry which enables efficient descrition of geometric objects such as line, plane and sphere geometry, as well the calculation of the intersection between such objects. The thesis presents a novel approach for the initial alignment between two point clouds called the Curvature-Based Descriptor. The curvature-based descriptor is a descriptor which describes the local curvature around a point in the point cloud. The local curvature is expressed with two spheres generated using conformal geometric algebra. The thesis also presents preprocessing steps which are used to segment the point cloud to extract only the parts of the point cloud that are necessary for the alignment, and a keypoint extraction method which extracts certain points from the point cloud, making the point correspondence more accurate. The inverse kinematics presented in this thesis is an analytic solution which uses conformal geometric algebra. The solution is presented for the Kuka KR6 R900 sixx robot and the Universal Robots UR5 robot. All singularities and all configurations are accounted for in the solutions. The thesis has several experimental results. These experiments are presented in each paper, and show the results from various methods performing point cloud alignment. The results show that it is possible to achieve a sub-millimeter accuracy for position estimation of an object using state-of-the-art methods when using both 3D and 2D cameras combined. The results also show that the curvature-based alignment method, after applying the preprocessing steps presented in the thesis, achieve a sub-millimeter accuracy on its own, an accuracy that is not achieved with any of the other 3D alignment methods

Inverse kinematics for industrial robots using conformal geometric algebra

This paper shows how the recently developed formulation of conformal geometric algebra can be used for analytic inverse kinematics of two six-link industrial manipulators with revolute joints. The paper demonstrates that the solution of the inverse kinematics in this framework relies on the intersection of geometric objects like lines, circles, planes and spheres, which provides the developer with valuable geometric intuition about the problem. It is believed that this will be very useful for new robot geometries and other mechanisms like cranes and topside drilling equipment. The paper extends previous results on inverse kinematics using conformal geometric algebra by providing consistent solutions for the joint angles for the different configurations depending on shoulder left or right, elbow up or down, and wrist flipped or not. Moreover, it is shown how to relate the solution to the Denavit-Hartenberg parameters of the robot. The solutions have been successfully implemented and tested extensively over the whole workspace of the manipulators

A Curvature-Based Descriptor for Point Cloud Alignment Using Conformal Geometric Algebra

This paper presents a descriptor for course alignment of point clouds using conformal geometric algebra. The method is based on selecting keypoints depending on shape factors to identify distinct features of the object represented by the point cloud, and a descriptor is then calculated for each keypoint by fitting two spheres that describe the local curvature. The method for estimating the point correspondences is to a larger extent based on geometric arguments than the method of Kleppe et al. (IEEE Trans Autom Sci Eng, 2017), which results in improved performance. The accuracy of the curvature-based descriptor is validated in experiments, and is shown to compare favorably to state-of-the-art methods in an experiment on course alignment of industrial parts to be assembled with robots.publishedVersion© The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made

Coarse Alignment for Model Fitting of Point Clouds Using a Curvature-Based Descriptor

This paper presents a method for coarse alignment of point clouds by introducing a new descriptor based on the local curvature. The method is developed for model fitting a CAD model for use in robotic assembly. The method is based on selecting keypoints depending on shape factors calculated from the local covariance matrix of the surface. A descriptor is then calculated for each keypoint by fitting two spheres that describe the curvature of the surface. The spheres are calculated using conformal geometric algebra, which gives a convenient and efficient description of the geometry. The keypoint descriptors for the model and the observed point cloud are then compared to estimate the corresponding keypoints, which are used to calculate the displacement. The method is tested in several experiments. One experiment is for robotic assembly, where objects are placed on a table and their position and orientation is estimated using a 3-D CAD model.acceptedVersion© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Automated assembly using 3D and 2D cameras

D and 3D computer vision systems are frequently being used in automated production to detect and determine the position of objects. Accuracy is important in the production industry, and computer vision systems require structured environments to function optimally. For 2D vision systems, a change in surfaces, lighting and viewpoint angles can reduce the accuracy of a method, maybe even to a degree that it will be erroneous, while for 3D vision systems, the accuracy mainly depends on the 3D laser sensors. Commercially available 3D cameras lack the precision found in high-grade 3D laser scanners, and are therefore not suited for accurate measurements in industrial use. In this paper, we show that it is possible to identify and locate objects using a combination of 2D and 3D cameras. A rough estimate of the object pose is first found using a commercially available 3D camera. Then, a robotic arm with an eye-in-hand 2D camera is used to determine the pose accurately. We show that this increases the accuracy to <1 and <1 . This was demonstrated in a real industrial assembly task where high accuracy is required

Object Detection in Point Clouds Using Conformal Geometric Algebra

This paper presents an approach for detecting primitive geometric objects in point clouds captured from 3D cameras. Primitive objects are objects that are well defined with parameters and mathematical relations, such as lines, spheres and ellipsoids. RANSAC, a robust parameter estimator that classifies and neglects outliers, is used for object detection. The primitives considered are modeled, filtered and fitted using the conformal model of geometric algebra. Methods for detecting planes, spheres and cylinders are suggested. Least squares fitting of spheres and planes to point data are done analytically with conformal geometric algebra, while a cylinder is fitted by defining a nonlinear cost function which is optimized using a nonlinear least squares solver. Furthermore, the suggested object detection scheme is combined with an octree sampling strategy that results in fast detection of multiple primitive objects in point clouds