Sparse octree algorithms for scalable dense volumetric tracking and mapping

This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM), the task of localising an agent within an unknown environment and at the same time building a representation of it. In particular, we tackle the fundamental scalability limitations of dense volumetric SLAM systems. We do so by proposing a highly efficient hierarchical data-structure based on octrees together with a set of algorithms to support the most compute-intensive operations in typical volumetric reconstruction pipelines. We employ our hierarchical representation in a novel dense pipeline based on occupancy probabilities. Crucially, the complete space representation encoded by the octree enables to demonstrate a fully integrated system in which tracking, mapping and occupancy queries can be performed seamlessly on a single coherent representation. While achieving accuracy either at par or better than the current state-of-the-art, we demonstrate run-time performance of at least an order of magnitude better than currently available hierarchical data-structures. Finally, we introduce a novel multi-scale reconstruction system that exploits our octree hierarchy. By adaptively selecting the appropriate scale to match the effective sensor resolution in both integration and rendering, we demonstrate better reconstruction results and tracking accuracy compared to single-resolution grids. Furthermore, we achieve much higher computational performance by propagating information up and down the tree in a lazy fashion, which allow us to reduce the computational load when updating distant surfaces. We have released our software as an open-source library, named supereight, which is freely available for the benefit of the wider community. One of the main advantages of our library is its flexibility. By carefully providing a set of algorithmic abstractions, supereight enables SLAM practitioners to freely experiment with different map representations with no intervention on the back-end library code and crucially, preserving performance. Our work has been adopted by robotics researchers in both academia and industry.Open Acces

Improving stability in Adaptive Distributed Parallel applications: a cooperative predictive approach

With this thesis we take a step further on improving reconfiguration decisions in adaptive distributed parallel computations. The concept of switching cost is introduced with the aim of reducing the amount of reconfigurations and of improving the reconfigurations stability in dynamic execution scenar- ios. Computation modules control is based on the Model-Based Predictive Control (MPC) approach. We study the effectiveness of this approach in parallel distributed computations, where each module cooperates to find global optimal reconfiguration trajectory. Experimental results are obtained by means of experiments performed in a simulation environment

Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper

In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future. The goal of this exploration is to reduce execution time while meeting our quality of result objectives. In previous work we showed for the first time that it is possible to map this application to power constrained embedded systems, highlighting that decision choices made at the algorithmic design-level have the most impact. As the algorithmic design space is too large to be exhaustively evaluated, we use a previously introduced multi-objective Random Forest Active Learning prediction framework dubbed HyperMapper, to find good algorithmic designs. We show that HyperMapper generalizes on a recent cutting edge 3D scene understanding algorithm and on a modern GPU-based computer architecture. HyperMapper is able to beat an expert human hand-tuning the algorithmic parameters of the class of Computer Vision applications taken under consideration in this paper automatically. In addition, we use crowd-sourcing using a 3D scene understanding Android app to show that the Pareto front obtained on an embedded system can be used to accelerate the same application on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from 2 to over 12.Comment: 10 pages, Keywords: design space exploration, machine learning, computer vision, SLAM, embedded systems, GPU, crowd-sourcin

Comparative Design Space Exploration of Dense and Semi-Dense SLAM

SLAM has matured significantly over the past few years, and is beginning to appear in serious commercial products. While new SLAM systems are being proposed at every conference, evaluation is often restricted to qualitative visualizations or accuracy estimation against a ground truth. This is due to the lack of benchmarking methodologies which can holistically and quantitatively evaluate these systems. Further investigation at the level of individual kernels and parameter spaces of SLAM pipelines is non-existent, which is absolutely essential for systems research and integration. We extend the recently introduced SLAMBench framework to allow comparing two state-of-the-art SLAM pipelines, namely KinectFusion and LSD-SLAM, along the metrics of accuracy, energy consumption, and processing frame rate on two different hardware platforms, namely a desktop and an embedded device. We also analyze the pipelines at the level of individual kernels and explore their algorithmic and hardware design spaces for the first time, yielding valuable insights.Comment: IEEE International Conference on Robotics and Automation 201

SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM

SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify the interface of such algorithms, or to perform a holistic comparison of their capabilities. This is a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phonebased AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs across SLAM systems

Navigating the Landscape for Real-time Localisation and Mapping for Robotics, Virtual and Augmented Reality

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.Comment: Proceedings of the IEEE 201

Control-theoretic Adaptation Strategies for Autonomic Reconfigurable Parallel Applications on Cloud Environments

Cloud Computing is a paradigm that enables the access to a set of shared networking and computing resources and high-level platforms and services through the exploitation of virtualization technologies. On Clouds, it is of relevant importance to make applications adaptive and reconfigurable, in the sense that the optimal configuration (satisfying desired QoS levels) should be dynamically changed in response to variations in the workload conditions and in the resource availability. Due to this fact, adaptation strategies have gained much attention over the last years. Properties like control optimality (finding proper trade-offs between contrasting QoS goals), reconfiguration stability (expressed as a function of the average time between consecutive reconfigurations) and reconfiguration amplitude (performing sequences of small modifications of the current configuration) are important aspects to consider. In order to meet these needs, we present a control-theoretic approach and we provide a first validation of our proposals, giving an insight about its applicability to Cloud environments. Keywords—Autonomic Computing, Parallel Computations, Reconfigurations, Model-based Predictive Control, Distributed Cooperative Optimization

Precision Endoscopy in Peroral Myotomies for Motility Disorders of the Upper Gastrointestinal Tract: Current Insights and Prospective Avenues—A Comprehensive Review

Our review delves into the realm of peroral endoscopic myotomies (POEMs) in the upper gastrointestinal tract (UGT). In recent years, POEMs have brought about a revolution in the treatment of UGT motility disorders. Esophageal POEM, the first to be introduced, has now been validated as the primary treatment for achalasia. Subsequently developed, G-POEM displays promising results in addressing refractory gastroparesis. Over time, multiple endoscopic myotomy techniques have emerged for the treatment of Zenker’s diverticulum, including Z-POEM, POES, and hybrid approaches. Despite the well-established efficacy outcomes, new challenges arise in the realm of POEMs in the UGT. For esophageal POEM, the future scenario lies in customizing the myotomy extent to the minimum necessary, while for G-POEM, it involves identifying patients who can optimally benefit from the treatment. For ZD, it is crucial to validate an algorithm that considers various myotomy options according to the diverticulum’s size and in relation to individual patients. These challenges align with the concept of precision endoscopy, personalizing the technique for each subject. Within our text, we comprehensively examine each myotomy technique, analyzing indications, outcomes, and adverse events. Additionally, we explore the emerging challenges posed by myotomies within the context of the evolving field of precision endoscopy