384 research outputs found
An Object SLAM Framework for Association, Mapping, and High-Level Tasks
Object SLAM is considered increasingly significant for robot high-level
perception and decision-making. Existing studies fall short in terms of data
association, object representation, and semantic mapping and frequently rely on
additional assumptions, limiting their performance. In this paper, we present a
comprehensive object SLAM framework that focuses on object-based perception and
object-oriented robot tasks. First, we propose an ensemble data association
approach for associating objects in complicated conditions by incorporating
parametric and nonparametric statistic testing. In addition, we suggest an
outlier-robust centroid and scale estimation algorithm for modeling objects
based on the iForest and line alignment. Then a lightweight and object-oriented
map is represented by estimated general object models. Taking into
consideration the semantic invariance of objects, we convert the object map to
a topological map to provide semantic descriptors to enable multi-map matching.
Finally, we suggest an object-driven active exploration strategy to achieve
autonomous mapping in the grasping scenario. A range of public datasets and
real-world results in mapping, augmented reality, scene matching,
relocalization, and robotic manipulation have been used to evaluate the
proposed object SLAM framework for its efficient performance.Comment: Accepted by IEEE Transactions on Robotics(T-RO
Self-Annotated 3D Geometric Learning for Smeared Points Removal
There has been significant progress in improving the accuracy and quality of
consumer-level dense depth sensors. Nevertheless, there remains a common depth
pixel artifact which we call smeared points. These are points not on any 3D
surface and typically occur as interpolations between foreground and background
objects. As they cause fictitious surfaces, these points have the potential to
harm applications dependent on the depth maps. Statistical outlier removal
methods fare poorly in removing these points as they tend also to remove actual
surface points. Trained network-based point removal faces difficulty in
obtaining sufficient annotated data. To address this, we propose a fully
self-annotated method to train a smeared point removal classifier. Our approach
relies on gathering 3D geometric evidence from multiple perspectives to
automatically detect and annotate smeared points and valid points. To validate
the effectiveness of our method, we present a new benchmark dataset: the Real
Azure-Kinect dataset. Experimental results and ablation studies show that our
method outperforms traditional filters and other self-annotated methods. Our
work is publicly available at
https://github.com/wangmiaowei/wacv2024_smearedremover.git.Comment: The paper is accepted at WACV2024(https://wacv2024.thecvf.com/
Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning
Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt.
Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit.
Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte
Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen.
Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt.
Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie
Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert.
Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver
(∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare
Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet
Implicit Object Pose Estimation on RGB Images Using Deep Learning Methods
With the rise of robotic and camera systems and the success of deep learning in computer vision,
there is growing interest in precisely determining object positions and orientations. This is crucial for
tasks like automated bin picking, where a camera sensor analyzes images or point clouds to guide a
robotic arm in grasping objects. Pose recognition has broader applications, such as predicting a
car's trajectory in autonomous driving or adapting objects in virtual reality based on the viewer's
perspective.
This dissertation focuses on RGB-based pose estimation methods that use depth information only
for refinement, which is a challenging problem. Recent advances in deep learning have made it
possible to predict object poses in RGB images, despite challenges like object overlap, object
symmetries and more.
We introduce two implicit deep learning-based pose estimation methods for RGB images, covering
the entire process from data generation to pose selection. Furthermore, theoretical findings on
Fourier embeddings are shown to improve the performance of the so-called implicit neural
representations - which are then successfully utilized for the task of implicit pose estimation
Target Tracking Using Optical Markers for Remote Handling in ITER
The thesis focuses on the development of a vision system to be used in the remote handling systems of the International Thermonuclear Experimental Rector - ITER. It presents and discusses a realistic solution to estimate the pose of key operational targets, while taking into account the specific needs and restrictions of the application.
The contributions to the state of the art are in two main fronts: 1) the development of optical markers that can withstand the extreme conditions in the environment; 2) the development of a robust marker detection and identification framework that can be effectively applied to different use cases. The markers’ locations and labels are used in computing the pose.
In the first part of the work, a retro reflective marker made up ITER compliant materials, particularly, fused silica and stainless steel, is designed. A methodology is proposed to optimize the markers’ performance. Highly distinguishable markers are manufactured and tested.
In the second part, a hybrid pipeline is proposed that detects uncoded markers in low resolution images using classical methods and identifies them using a machine learning approach. It is demonstrated that the proposed methodology effectively generalizes to different marker constellations and can successfully detect both retro reflective markers and laser engravings.
Lastly, a methodology is developed to evaluate the end-to-end accuracy of the proposed solution using the feedback provided by an industrial robotic arm. Results are evaluated in a realistic test setup for two significantly different use cases.
Results show that marker based tracking is a viable solution for the problem at hand and can provide superior performance to the earlier stereo matching based approaches. The developed solutions could be applied to other use cases and applications
Methods, Models, and Datasets for Visual Servoing and Vehicle Localisation
Machine autonomy has become a vibrant part of industrial and commercial aspirations. A growing demand exists for dexterous and intelligent machines that can work in unstructured environments without any human assistance. An autonomously operating machine should sense its surroundings, classify different kinds of observed objects, and interpret sensory information to perform necessary operations.
This thesis summarizes original methods aimed at enhancing machine’s autonomous operation capability. These methods and the corresponding results are grouped into two main categories. The first category consists of research works that focus on improving visual servoing systems for robotic manipulators to accurately position workpieces. We start our investigation with the hand-eye calibration problem that focuses on calibrating visual sensors with a robotic manipulator. We thoroughly investigate the problem from various perspectives and provide alternative formulations of the problem and error objectives. The experimental results demonstrate that the proposed methods are robust and yield accurate solutions when tested on real and simulated data. The work package is bundled as a toolkit and available online for public use. In an extension, we proposed a constrained multiview pose estimation approach for robotic manipulators. The approach exploits the available geometric constraints on the robotic system and infuses them directly into the pose estimation method. The empirical results demonstrate higher accuracy and significantly higher precision compared to other studies.
In the second part of this research, we tackle problems pertaining to the field of autonomous vehicles and its related applications. First, we introduce a pose estimation and mapping scheme to extend the application of visual Simultaneous Localization and Mapping to unstructured dynamic environments. We identify, extract, and discard dynamic entities from the pose estimation step. Moreover, we track the dynamic entities and actively update the map based on changes in the environment. Upon observing the limitations of the existing datasets during our earlier work, we introduce FinnForest, a novel dataset for testing and validating the performance of visual odometry and Simultaneous Localization and Mapping methods in an un-structured environment. We explored an environment with a forest landscape and recorded data with multiple stereo cameras, an IMU, and a GNSS receiver. The dataset offers unique challenges owing to the nature of the environment, variety of trajectories, and changes in season, weather, and daylight conditions. Building upon the future works proposed in FinnForest Dataset, we introduce a novel scheme that can localize an observer with extreme perspective changes. More specifically, we tailor the problem for autonomous vehicles such that they can recognize a previously visited place irrespective of the direction it previously traveled the route. To the best of our knowledge, this is the first study that accomplishes bi-directional loop closure on monocular images with a nominal field of view. To solve the localisation problem, we segregate the place identification from the pose regression by using deep learning in two steps. We demonstrate that bi-directional loop closure on monocular images is indeed possible when the problem is posed correctly, and the training data is adequately leveraged.
All methodological contributions of this thesis are accompanied by extensive empirical analysis and discussions demonstrating the need, novelty, and improvement in performance over existing methods for pose estimation, odometry, mapping, and place recognition
PhD students´day FMST 2023
The authors gave oral presentations of their work online as part of a Doctoral Students’ Day held on 15 June 2023, and they reflect the challenging work done by the students and their supervisors in the fields of metallurgy, materials engineering and management. There are 82 contributions in total, covering a range of areas – metallurgical technology, thermal engineering and fuels in industry, chemical metallurgy, nanotechnology, materials science and engineering, and industrial systems management. This represents a cross-section of the diverse topics investigated by doctoral students at the faculty, and it will provide a guide for Master’s graduates in these or similar disciplines who are interested in pursuing their scientific careers further, whether they are from the faculty here in Ostrava or engineering faculties elsewhere in the Czech Republic. The quality of the contributions varies: some are of average quality, but many reach a standard comparable with research articles published in established journals focusing on disciplines of materials technology. The diversity of topics, and in some cases the excellence of the contributions, with logical structure and clearly formulated conclusions, reflect the high standard of the doctoral programme at the faculty.Ostrav
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