A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RGBD as well as 3D LIDAR data. In contrast to popular point cloud registration approaches such as ICP our method does not rely on explicit data association and exploits multiple modalities such as raw range and image data streams. Color, depth, and normal information are handled in an uniform manner and the registration is obtained by minimizing the pixel-wise difference between two multi-channel images. We developed a flexible and general framework and implemented our approach inside that framework. We also released our implementation as open source C++ code. The experiments show that our approach allows for an accurate registration of the sensor data without requiring an explicit data association or model-specific adaptations to datasets or sensors. Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.Comment: 8 page

Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement

In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error. This enables a higher accuracy in the initial part of the receding horizon, which is more relevant to NMPC, while keeping bounded the number of discretization points. By combining this feature with an efficient Least Square formulation, our solver is also extremely time-efficient, generating trajectories of multiple seconds within only a few milliseconds. The performance of the proposed approach has been validated in a high fidelity simulation environment, by using an UAV platform. We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR 2018

Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use

Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field. Currently, there are many open-source systems that are able to deliver fast and accurate estimation in typical real-world scenarios. Still, all these systems often provide an ad-hoc implementation that entailed to predefined sensor configurations. In this work, we tackle this issue, proposing a novel SLAM architecture specifically designed to address heterogeneous sensors' configuration and to standardize SLAM solutions. Thanks to its modularity and to specific design patterns, the presented architecture is easy to extend, enhancing code reuse and efficiency. Finally, adopting our solution, we conducted comparative experiments for a variety of sensor configurations, showing competitive results that confirm state-of-the-art performance

The ARCH Projects: design and rationale (IAASSG 001)

OBJECTIVE A number of factors limit the effectiveness of current aortic arch studies in assessing optimal neuroprotection strategies, including insufficient patient numbers, heterogenous definitions of clinical variables, multiple technical strategies, inadequate reporting of surgical outcomes and a lack of collaborative effort. We have formed an international coalition of centres to provide more robust investigations into this topic. METHODS High-volume aortic arch centres were identified from the literature and contacted for recruitment. A Research Steering Committee of expert arch surgeons was convened to oversee the direction of the research. RESULTS The International Aortic Arch Surgery Study Group has been formed by 41 arch surgeons from 10 countries to better evaluate patient outcomes after aortic arch surgery. Several projects, including the establishment of a multi-institutional retrospective database, randomized controlled trials and a prospectively collected database, are currently underway. CONCLUSIONS Such a collaborative effort will herald a turning point in the surgical management of aortic arch pathologies and will provide better powered analyses to assess the impact of varying surgical techniques on mortality and morbidity, identify predictors for neurological and operative risk, formulate and validate risk predictor models and review long-term survival outcomes and quality-of-life after arch surger

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Leveraging Least Squares for a Unified Methodology in Mobile Robotics and SLAM problems

Intelligent mobile robots require a model of the operating environment to perform their tasks. For example, a vacuum cleaner robot needs a model of the house to efficently cover the space, and to visit all the rooms. An autonomous car needs a model of the surrounding environment, i.e. the traversed city, to move from its starting location to its goal one. Instead, a robot designed for planetary exploration should be able to build such a model, with the goal of safely exploring the surrounding environment. Building such robust systems requires performing a set of mandatory steps. Namely, all the intrinsics and extrinsics parameters of the robot and of the sensors mounted on it has to be accurately estimated; using the sensors readings, the platform has to build a compress, yet informative, representation of the environment; the robot has to be constantly localized in this representation; and it has to safely navigate into it. Calibration, Simultaneous Localization and Mapping (SLAM) and Navigation constitute active fields of research, leading to robust and reliable industrial products. In these thesis we faced all these problems, providing novel contributions to the state- of-the-art approaches. The solution of all these tasks requires the application of a coherent methodology, leveraging least squares solvers. Thus, the contribution of this thesis is twofold. First, we provide novel contributions to calibration-, SLAM- and navigation-related problems, with a particular focus on motion-based calibration, feature-less point cloud registration, environment representation using high-level primitives, and model predictive control. Second, we provide a unified methodology, leveraging least squares, to design and solve mobile robotics and SLAM related problems. The presented methodology is intended to be used as a guideline to face such problems, requiring specific adaptations for each specific application. Thus, for each faced problem we provide the specializations needed to achieve state-of-the-art performance. Moreover, to foster the repeatability of our experiments, we provide our open-source implementation for each one of the solutions presented in this thesis

Unsupervised calibration of wheeled mobile platforms

This paper describes an unsupervised approach to retrieve the kinematic parameters of a wheeled mobile robot. The robot chooses which action to take in order to minimize the uncertainty in the parameter estimate and to fully explore the parameter space. Our method explores the effects of a set of elementary motion on the platform to dynamically select the best action and to stop the process when the estimate can be no further improved. We tested our approach both in simulation and with real robots. Our method is reported to obtain in shorter time parameter estimates that are statistically more accurate than the ones obtained by steering the robot on predefined patterns

3-D Map Merging on Pose Graphs

In this letter, we propose an approach for merging three-dimensional maps represented as pose graphs of point clouds. Our method can effectively deal with typical distortions affecting simultaneous localization and mapping-generated maps. Traditional map merging techniques that use a single rigid body transformation to relate the reference frames of different maps. Instead, our approach achieves more accurate results by eliminating the inconsistencies resulting from distortions affecting the inputs, and can succeed in those situations where traditional approaches fail for substantial deformations. The core idea behind our solution is to localize the robot in a reference map by using the data from another map as observations. We validated our approach on publicly available datasets, and provide quantitative results that confirm its effectiveness on challenging instances of the merging problem