arXiv subjects

Markus Vincze

Publications and source records attributed to Markus Vincze.

50 records · Page 3Linked to original sources

arXiv2019

In-pipe Robotic System for Pipe-joint Rehabilitation in Fresh Water Pipes

The robot's objective is to rehabilitate the pipe joints of fresh water supply systems by crawling into water canals and applying a restoration material to repair the pipes. The robot's structure consists of six wheeled-legs, three on the front separated 120° and three on the back in the same configuration, supporting the structure along the centre of the pipe. In this configuration the robot is able to clean and seal with a rotating tool, similar to a cylindrical robot, covering the entire 3D in-pipe space.

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arXiv2019

Addressing the Sim2Real Gap in Robotic 3D Object Classification

Object classification with 3D data is an essential component of any scene understanding method. It has gained significant interest in a variety of communities, most notably in robotics and computer graphics. While the advent of deep learning has progressed the field of 3D object classification, most work using this data type are solely evaluated on CAD model datasets. Consequently, current work does not address the discrepancies existing between real and artificial data. In this work, we examine this gap in a robotic context by specifically addressing the problem of classification when transferring from artificial CAD models to real reconstructed objects. This is performed by training on ModelNet (CAD models) and evaluating on ScanNet (reconstructed objects). We show that standard methods do not perform well in this task. We thus introduce a method that carefully samples object parts that are reproducible under various transformations and hence robust. Using graph convolution to classify the composed graph of parts, our method significantly improves upon the baseline.

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arXiv2019

Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation

Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries. It is also difficult to construct 3D models with precise texture without expert knowledge or specialized scanning devices. To address these problems, we propose a novel pose estimation method, Pix2Pose, that predicts the 3D coordinates of each object pixel without textured models. An auto-encoder architecture is designed to estimate the 3D coordinates and expected errors per pixel. These pixel-wise predictions are then used in multiple stages to form 2D-3D correspondences to directly compute poses with the PnP algorithm with RANSAC iterations. Our method is robust to occlusion by leveraging recent achievements in generative adversarial training to precisely recover occluded parts. Furthermore, a novel loss function, the transformer loss, is proposed to handle symmetric objects by guiding predictions to the closest symmetric pose. Evaluations on three different benchmark datasets containing symmetric and occluded objects show our method outperforms the state of the art using only RGB images.

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arXiv2019

EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets

Developing robot perception systems for recognizing objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms. This paper presents the EasyLabel tool for easily acquiring high quality ground truth annotation of objects at the pixel-level in densely cluttered scenes. In a semi-automatic process, complex scenes are incrementally built and EasyLabel exploits depth change to extract precise object masks at each step. We use this tool to generate the Object Cluttered Indoor Dataset (OCID) that captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. OCID is used to perform a systematic comparison of existing object segmentation methods. The baseline comparison supports the need for pixel- and object-wise annotation to progress robot vision towards realistic applications. This insight reveals the usefulness of EasyLabel and OCID to better understand the challenges that robots face in the real-world. Copyright 20XX 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.

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arXiv2019

Recurrent Convolutional Fusion for RGB-D Object Recognition

Providing machines with the ability to recognize objects like humans has always been one of the primary goals of machine vision. The introduction of RGB-D cameras has paved the way for a significant leap forward in this direction thanks to the rich information provided by these sensors. However, the machine vision community still lacks an effective method to synergically use the RGB and depth data to improve object recognition. In order to take a step in this direction, we introduce a novel end-to-end architecture for RGB-D object recognition called recurrent convolutional fusion (RCFusion). Our method generates compact and highly discriminative multi-modal features by combining complementary RGB and depth information representing different levels of abstraction. Extensive experiments on two popular datasets, RGB-D Object Dataset and JHUIT-50, show that RCFusion significantly outperforms state-of-the-art approaches in both the object categorization and instance recognition tasks.

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arXiv2018

Recognizing Objects In-the-wild: Where Do We Stand?

The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, preventing the use of autonomous agents for real-world applications. The progress is slowed down by the lack of a testbed able to accurately represent the world perceived by the robot in-the-wild. In order to fill this gap, we introduce a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. The dataset embeds the challenges faced by a robot in a real-life application and provides a useful tool for validating object recognition algorithms. Besides describing the characteristics of the dataset, the paper evaluates the performance of a collection of well-established deep convolutional networks on the new dataset and analyzes the transferability of deep representations from Web images to robotic data. Despite the promising results obtained with such representations, the experiments demonstrate that object classification with real-life robotic data is far from being solved. Finally, we provide a comparative study to analyze and highlight the open challenges in robot vision, explaining the discrepancies in the performance.

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arXiv2018

High Dynamic Range SLAM with Map-Aware Exposure Time Control

The research in dense online 3D mapping is mostly focused on the geometrical accuracy and spatial extent of the reconstructions. Their color appearance is often neglected, leading to inconsistent colors and noticeable artifacts. We rectify this by extending a state-of-the-art SLAM system to accumulate colors in HDR space. We replace the simplistic pixel intensity averaging scheme with HDR color fusion rules tailored to the incremental nature of SLAM and a noise model suitable for off-the-shelf RGB-D cameras. Our main contribution is a map-aware exposure time controller. It makes decisions based on the global state of the map and predicted camera motion, attempting to maximize the information gain of each observation. We report a set of experiments demonstrating the improved texture quality and advantages of using the custom controller that is tightly integrated in the mapping loop.

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arXiv2016

The STRANDS Project: Long-Term Autonomy in Everyday Environments

Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance.

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arXiv2016

Help, Anyone? A User Study For Modeling Robotic Behavior To Mitigate Malfunctions With The Help Of The User

Service robots for the domestic environment are intended to autonomously provide support for their users. However, state-of-the-art robots still often get stuck in failure situations leading to breakdowns in the interaction flow from which the robot cannot recover alone. We performed a multi-user Wizard-of-Oz experiment in which we manipulated the robot's behavior in such a way that it appeared unexpected and malfunctioning, and asked participants to help the robot in order to restore the interaction flow. We examined how participants reacted to the robot's error, its subsequent request for help and how it changed their perception of the robot with respect to perceived intelligence, likability, and task contribution. As interaction scenario we used a game of building Lego models performed by user dyads. In total 38 participants interacted with the robot and helped in malfunctioning situations. We report two major findings: (1) in user dyads, the user who gave the last command followed by the user who is closer is more likely to help (2) malfunctions that can be actively fixed by the user seem not to negatively impact perceived intelligence and likability ratings. This work offers insights in how far user support can be a strategy for domestic service robots to recover from repeating malfunctions.

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arXiv2015

Where to look first? Behaviour control for fetch-and-carry missions of service robots

This paper presents the behaviour control of a service robot for intelligent object search in a domestic environment. A major challenge in service robotics is to enable fetch-and-carry missions that are satisfying for the user in terms of efficiency and human-oriented perception. The proposed behaviour controller provides an informed intelligent search based on a semantic segmentation framework for indoor scenes and integrates it with object recognition and grasping. Instead of manually annotating search positions in the environment, the framework automatically suggests likely locations to search for an object based on contextual information, e.g. next to tables and shelves. In a preliminary set of experiments we demonstrate that this behaviour control is as efficient as using manually annotated locations. Moreover, we argue that our approach will reduce the intensity of labour associated with programming fetch-and-carry tasks for service robots and that it will be perceived as more human-oriented.

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arXiv2015

Using Dimension Reduction to Improve the Classification of High-dimensional Data

In this work we show that the classification performance of high-dimensional structural MRI data with only a small set of training examples is improved by the usage of dimension reduction methods. We assessed two different dimension reduction variants: feature selection by ANOVA F-test and feature transformation by PCA. On the reduced datasets, we applied common learning algorithms using 5-fold cross-validation. Training, tuning of the hyperparameters, as well as the performance evaluation of the classifiers was conducted using two different performance measures: Accuracy, and Receiver Operating Characteristic curve (AUC). Our hypothesis is supported by experimental results.

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arXiv2015

Object Modelling with a Handheld RGB-D Camera

This work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor. A major advantage of the method is that our reconstruction pipeline allows the user to acquire a full 3D model of the object. This is achieved by acquiring several partial 3D models in different sessions that are automatically merged together to reconstruct a full model. In addition, the 3D models acquired by our system can be directly used by state-of-the-art object instance recognition and object tracking modules, providing object-perception capabilities for different applications, such as human-object interaction analysis or robot grasping. The system does not impose constraints in the appearance of objects (textured, untextured) nor in the modelling setup (moving camera with static object or a turn-table setup). The proposed reconstruction system has been used to model a large number of objects resulting in metrically accurate and visually appealing 3D models.

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arXiv2014

Find my mug: Efficient object search with a mobile robot using semantic segmentation

In this paper, we propose an efficient semantic segmentation framework for indoor scenes, tailored to the application on a mobile robot. Semantic segmentation can help robots to gain a reasonable understanding of their environment, but to reach this goal, the algorithms not only need to be accurate, but also fast and robust. Therefore, we developed an optimized 3D point cloud processing framework based on a Randomized Decision Forest, achieving competitive results at sufficiently high frame rates. We evaluate the capabilities of our method on the popular NYU depth dataset and our own data and demonstrate its feasibility by deploying it on a mobile service robot, for which we could optimize an object search procedure using our results.

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arXiv2013

Visual Room-Awareness for Humanoid Robot Self-Localization

Humanoid robots without internal sensors such as a compass tend to lose their orientation after a fall. Furthermore, re-initialisation is often ambiguous due to symmetric man-made environments. The room-awareness module proposed here is inspired by the results of psychological experiments and improves existing self-localization strategies by mapping and matching the visual background with colour histograms. The matching algorithm uses a particle-filter to generate hypotheses of the viewing directions independent of the self-localization algorithm and generates confidence values for various possible poses. The robot's behaviour controller uses those confidence values to control self-localization algorithm to converge to the most likely pose and prevents the algorithm from getting stuck in local minima. Experiments with a symmetric Standard Platform League RoboCup playing field with a simulated and a real humanoid NAO robot show the significant improvement of the system.

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