Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin assert joint first authorshi

Evolution of Neural Networks Through Incremental Acquisition of Neural Structures

In this contribution we present a novel method, called Evolutionary Acquisition of Neural Topologies (EANT), of evolving the structures and weights of neural networks. The method introduces an efficient and compact genetic encoding of a neural network onto a linear genome that enables one to evaluate the network without decoding it. The method uses a meta-level evolutionary process where new structures are explored at larger time-scale and the existing structures are exploited at lower time-scale. This enables it to find minimal neural structures for solving a given learning task

Towards an integrated understanding of neural networks

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 123-136).Neural networks underpin both biological intelligence and modern Al systems, yet there is relatively little theory for how the observed behavior of these networks arises. Even the connectivity of neurons within the brain remains largely unknown, and popular deep learning algorithms lack theoretical justification or reliability guarantees. This thesis aims towards a more rigorous understanding of neural networks. We characterize and, where possible, prove essential properties of neural algorithms: expressivity, learning, and robustness. We show how observed emergent behavior can arise from network dynamics, and we develop algorithms for learning more about the network structure of the brain.by David Rolnick.Ph. D

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Computational studies of genome evolution and regulation

This thesis takes on the challenge of extracting information from large volumes of biological data produced with newly established experimental techniques. The different types of information present in a particular dataset have been carefully identified to maximise the information gained from the data. This also precludes the attempts to infer the types of information that are not present in the data. In the first part of the thesis I examined the evolutionary origins of de novo taxonomically restricted genes (TRGs) in Drosophila subgenus. De novo TRGs are genes that have originated after the speciation of a particular clade from previously non-coding regions - functional ncRNA, within introns or alternative frames of older protein-coding genes, or from intergenic sequences. TRGs are clade-specific tool-kits that are likely to contain proteins with yet undocumented functions and new protein folds that are yet to be discovered. One of the main challenges in studying de novo TRGs is the trade-off between false positives (non-functional open reading frames) and false negatives (true TRGs that have properties distinct from well established genes). Here I identified two de novo TRG families in Drosophila subgenus that have not been previously reported as de novo originated genes, and to our knowledge they are the best candidates identified so far for experimental studies aimed at elucidating the properties of de novo genes. In the second part of the thesis I examined the information contained in single cell RNA sequencing (scRNA-seq) data and propose a method for extracting biological knowledge from this data using generative neural networks. The main challenge is the noisiness of scRNA-seq data - the number of transcripts sequenced is not proportional to the number of mRNAs present in the cell. I used an autoencoder to reduce the dimensionality of the data without making untestable assumptions about the data. This embedding into lower dimensional space alongside the features learned by an autoencoder contains information about the cell populations, differentiation trajectories and the regulatory relationships between the genes. Unlike most methods currently used, an autoencoder does not assume that these regulatory relationships are the same in all cells in the data set. The main advantages of our approach is that it makes minimal assumptions about the data, it is robust to noise and it is possible to assess its performance. In the final part of the thesis I summarise lessons learnt from analysing various types of biological data and make suggestions for the future direction of similar computational studies

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein