Fast Graph-Based Object Segmentation for RGB-D Images

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph- based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with O(NlogN) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods

Physical Interaction of Autonomous Robots in Complex Environments

Recent breakthroughs in the fields of computer vision and robotics are firmly changing the people perception about robots. The idea of robots that substitute humansisnowturningintorobotsthatcollaboratewiththem. Serviceroboticsconsidersrobotsaspersonalassistants. Itsafelyplacesrobotsindomesticenvironments in order to facilitate humans daily life. Industrial robotics is now reconsidering its basic idea of robot as a worker. Currently, the primary method to guarantee the personnels safety in industrial environments is the installation of physical barriers around the working area of robots. The development of new technologies and new algorithms in the sensor field and in the robotic one has led to a new generation of lightweight and collaborative robots. Therefore, industrial robotics leveraged the intrinsic properties of this kind of robots to generate a robot co-worker that is able to safely coexist, collaborate and interact inside its workspace with both personnels and objects. This Ph.D. dissertation focuses on the generation of a pipeline for fast object pose estimation and distance computation of moving objects,in both structured and unstructured environments,using RGB-D images. This pipeline outputs the command actions which let the robot complete its main task and fulfil the safety human-robot coexistence behaviour at once. The proposed pipeline is divided into an object segmentation part,a 6D.o.F. object pose estimation part and a real-time collision avoidance part for safe human-robot coexistence. Firstly, the segmentation module finds candidate object clusters out of RGB-D images of clutter scenes using a graph-based image segmentation technique. This segmentation technique generates a cluster of pixels for each object found in the image. The candidate object clusters are then fed as input to the 6 D.o.F. object pose estimation module. The latter is in charge of estimating both the translation and the orientation in 3D space of each candidate object clusters. The object pose is then employed by the robotic arm to compute a suitable grasping policy. The last module generates a force vector field of the environment surrounding the robot, the objects and the humans. This force vector field drives the robot toward its goal while any potential collision against objects and/or humans is safely avoided. This work has been carried out at Politecnico di Torino, in collaboration with Telecom Italia S.p.A

A Low-cost Open Source 3D-Printable Dexterous Anthropomorphic Robotic Hand with a Parallel Spherical Joint Wrist for Sign Languages Reproduction

We present a novel open-source 3D-printable dexterous anthropomorphic robotic hand specifically designed to reproduce Sign Languages' hand poses for deaf and deaf-blind users. We improved the InMoov hand, enhancing dexterity by adding abduction/adduction degrees of freedom of three fingers (thumb, index and middle fingers) and a three-degrees-of-freedom parallel spherical joint wrist. A systematic kinematic analysis is provided. The proposed robotic hand is validated in the framework of the PARLOMA project. PARLOMA aims at developing a telecommunication system for deaf-blind people, enabling remote transmission of signs from tactile Sign Languages. Both hardware and software are provided online to promote further improvements from the community

Clinical features and outcomes of elderly hospitalised patients with chronic obstructive pulmonary disease, heart failure or both

Background and objective: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) mutually increase the risk of being present in the same patient, especially if older. Whether or not this coexistence may be associated with a worse prognosis is debated. Therefore, employing data derived from the REPOSI register, we evaluated the clinical features and outcomes in a population of elderly patients admitted to internal medicine wards and having COPD, HF or COPD + HF. Methods: We measured socio-demographic and anthropometric characteristics, severity and prevalence of comorbidities, clinical and laboratory features during hospitalization, mood disorders, functional independence, drug prescriptions and discharge destination. The primary study outcome was the risk of death. Results: We considered 2,343 elderly hospitalized patients (median age 81 years), of whom 1,154 (49%) had COPD, 813 (35%) HF, and 376 (16%) COPD + HF. Patients with COPD + HF had different characteristics than those with COPD or HF, such as a higher prevalence of previous hospitalizations, comorbidities (especially chronic kidney disease), higher respiratory rate at admission and number of prescribed drugs. Patients with COPD + HF (hazard ratio HR 1.74, 95% confidence intervals CI 1.16-2.61) and patients with dementia (HR 1.75, 95% CI 1.06-2.90) had a higher risk of death at one year. The Kaplan-Meier curves showed a higher mortality risk in the group of patients with COPD + HF for all causes (p = 0.010), respiratory causes (p = 0.006), cardiovascular causes (p = 0.046) and respiratory plus cardiovascular causes (p = 0.009). Conclusion: In this real-life cohort of hospitalized elderly patients, the coexistence of COPD and HF significantly worsened prognosis at one year. This finding may help to better define the care needs of this population

Fast Feature-Less Quaternion-based Particle Swarm Optimization for Object Pose Estimation From RGB-D Images

We present a novel quaternion-based formulation of Particle Swarm Optimization for pose estimation which, differently from other approaches, does not rely on image features or machine learning. The quaternion formulation avoids the gimbal lock problem, and the objective function is based on raw 2D depth information only, under the assumption that the object region is segmented from the background. This makes the algorithm suit- able for pose estimation of objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. We find candidate object regions using a graph-based image segmentation approach that integrates color and depth infor- mation, but the PSO is agnostic to the segmentation algorithm used. The algorithm is implemented on GPU, and the nature of the objective function allows high paralleliza- tion. We test the approach on different publicly available RGB-D object datasets, discuss the results and compare them with other existing methods

Fast Feature-Less Quaternion-based Particle Swarm Optimization for Object Pose Estimation From RGB-D Images

We present a novel quaternion-based formulation of Particle Swarm Optimization for pose estimation which, differently from other approaches, does not rely on image features or machine learning. The quaternion formulation avoids the gimbal lock problem, and the objective function is based on raw 2D depth information only, under the assumption that the object region is segmented from the background. This makes the algorithm suit- able for pose estimation of objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. We find candidate object regions using a graph-based image segmentation approach that integrates color and depth infor- mation, but the PSO is agnostic to the segmentation algorithm used. The algorithm is implemented on GPU, and the nature of the objective function allows high paralleliza- tion. We test the approach on different publicly available RGB-D object datasets, discuss the results and compare them with other existing method

Q-PSO: fast quaternion-based pose estimation from RGB-D images

We present a pipeline for fast object pose estimation using RGB-D images, which does not rely on image features or machine learning.We are interested in segmenting objects with large variety in appearance, from lack of texture to presence of strong textures, with a focus on the task of robotic grasping. The proposed pipeline is divided into an object segmentation part and a pose estimation part. We first find candidate object clusters using a graph-based image segmentation technique. A modified Canny edge detector is introduced for extracting robust graph edges by fusing RGB and depth information. A suitable cost function is used for building the graph, which is then partitioned using the concept of internal and external differences between graph regions. The extracted object regions are then used to initialize the 3D position of a quaternion-based Particle Swarm Optimization algorithm (Q-PSO), that fits a 3D model of the object to the depth image. The fitness function is based on depth information only and the quaternion formulation avoids singularities and the need for conversions between rotation representations. In this work we focus on the details of the GPU implementation of Q-PSO, in order to fully exploit the highly parallelizable nature of the particular implementation of the particle swarm algorithm, and discuss critic implementation details. We then test the approach on different publicly available RGB-D object datasets, and provide numeric comparisons with other state-of-the-art methods, as well as a discussion on robustness and an extension to the case of articulated objects. We show how Q-PSO offers comparable performances to current learning-based approaches,while not suffering from the problems of lack of features in objects or issues related to training, such as the need for a large training set and long training times

Q-PSO: fast quaternion-based pose estimation from RGB-D images

We present a pipeline for fast object pose estimation using RGB-D images, which does not rely on image features or machine learning.We are interested in segmenting objects with large variety in appearance, from lack of texture to presence of strong textures, with a focus on the task of robotic grasping. The proposed pipeline is divided into an object segmentation part and a pose estimation part. We first find candidate object clusters using a graph-based image segmentation technique. A modified Canny edge detector is introduced for extracting robust graph edges by fusing RGB and depth information. A suitable cost function is used for building the graph, which is then partitioned using the concept of internal and external differences between graph regions. The extracted object regions are then used to initialize the 3D position of a quaternion-based Particle Swarm Optimization algorithm (Q-PSO), that fits a 3D model of the object to the depth image. The fitness function is based on depth information only and the quaternion formulation avoids singularities and the need for conversions between rotation representations. In this work we focus on the details of the GPU implementation of Q-PSO, in order to fully exploit the highly parallelizable nature of the particular implementation of the particle swarm algorithm, and discuss critic implementation details. We then test the approach on different publicly available RGB-D object datasets, and provide numeric comparisons with other state-of-the-art methods, as well as a discussion on robustness and an extension to the case of articulated objects. We show how Q-PSO offers comparable performances to current learning-based approaches,while not suffering from the problems of lack of features in objects or issues related to training, such as the need for a large training set and long training times