| 11. |
- Nalpantidis, Lazaros, et al.
(författare)
-
Computationally effective stereovision SLAM
- 2010
-
Ingår i: 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings. - : IEEE. - 9781424464944 ; , s. 458-463
-
Konferensbidrag (refereegranskat)abstract
- In this paper a visual Simultaneous Localization and Mapping (SLAM) algorithm suitable for indoor area measurement applications is proposed. The algorithm is focused on computational effectiveness. The only sensor used is a stereo camera placed onboard a moving robot. The algorithm processes the acquired images calculating the depth of the scenery, detecting occupied areas and progressively building a map of the environment. The stereo vision-based SLAM algorithm embodies a custom-tailored stereo correspondence algorithm, the robust scale and rotation invariant feature detection and matching Speeded Up Robust Features (SURF) method, a computationally effective v-disparity image calculation scheme, a novel map-merging module, as well as a sophisticated Cellular Automata (CA)-based enhancement stage. The proposed algorithm is suitable for autonomously mapping and measuring indoor areas using robots. The algorithm is presented and experimental results for self-captured image sets are provided and analyzed.
|
|
| 12. |
- Porathe, Thomas, 1954, et al.
(författare)
-
Maritime Unmanned Navigation through Intelligence in Networks: The MUNIN project
- 2013
-
Ingår i: 12th International Conference on Computer and IT Applications in the Maritime Industries, COMPIT’13, Cortona, 15-17 April 2013. - 9783892206637 ; , s. 177-183
-
Konferensbidrag (refereegranskat)abstract
- This paper introduces the MUNIN project attempting to put a 200 meter long bulk carrier under autonomous control. The paper gives a motivation and an overview of the project as well as present some of the key research questions dealing with the human intervention possibilities. As a fallback option the unmanned ship is monitored by a shore control center which has the ability to take direct control if necessary. A challenge for the unmanned ship is the interaction with other manned ships.
|
|
| 13. |
- Tang, R., et al.
(författare)
-
A literature review of Artificial Intelligence applications in railway systems
- 2022
-
Ingår i: Transportation Research Part C. - : Elsevier Ltd. - 0968-090X .- 1879-2359. ; 140
-
Tidskriftsartikel (refereegranskat)abstract
- Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.
|
|
| 14. |
- Ringdahl, Ola, 1971-, et al.
(författare)
-
Performance of RGB-D camera for different object types in greenhouse conditions
- 2019
-
Ingår i: 2019 European conference on mobile robots (ECMR). - : IEEE. - 9781728136066 - 9781728136059
-
Konferensbidrag (refereegranskat)abstract
- RGB-D cameras play an increasingly important role in localization and autonomous navigation of mobile robots. Reasonably priced commercial RGB-D cameras have recently been developed for operation in greenhouse and outdoor conditions. They can be employed for different agricultural and horticultural operations such as harvesting, weeding, pruning and phenotyping. However, the depth information extracted from the cameras varies significantly between objects and sensing conditions. This paper presents an evaluation protocol applied to a commercially available Fotonic F80 time-of-flight RGB-D camera for eight different object types. A case study of autonomous sweet pepper harvesting was used as an exemplary agricultural task. Each of the objects chosen is a possible item that an autonomous agricultural robot must detect and localize to perform well. A total of 340 rectangular regions of interests (ROI) were marked for the extraction of performance measures of point cloud density, and variability around center of mass, 30-100 ROIs per object type. An additional 570 ROIs were generated (57 manually and 513 replicated) to evaluate the repeatability and accuracy of the point cloud. A statistical analysis was performed to evaluate the significance of differences between object types. The results show that different objects have significantly different point density. Specifically metallic materials and black colored objects had significantly less point density compared to organic and other artificial materials introduced to the scene as expected. The point cloud variability measures showed no significant differences between object types, except for the metallic knife that presented significant outliers in collected measures. The accuracy and repeatability analysis showed that 1-3 cm errors are due to the the difficulty for a human to annotate the exact same area and up to ±4 cm error is due to the sensor not generating the exact same point cloud when sensing a fixed object.
|
|
| 15. |
- Chen, D., et al.
(författare)
-
Artificial intelligence enabled Digital Twins for training autonomous cars
- 2022
-
Ingår i: Internet of Things and Cyber-Physical Systems. - : KeAi Communications Co.. - 2667-3452. ; 2, s. 31-41
-
Tidskriftsartikel (refereegranskat)abstract
- This exploration is aimed at the system prediction and safety performance of the Digital Twins (DTs) of autonomous cars based on artificial intelligence technology, and the intelligent development of transportation in the smart city. On the one hand, considering the problem of safe driving of autonomous cars in intelligent transportation systems, it is essential to ensure the transmission safety of vehicle data and realize the load balancing scheduling of data transmission resources. On the other hand, convolution neural network (CNN) of the deep learning algorithm is adopted and improved, and then, the DTs technology is introduced. Finally, an autonomous cars DTs prediction model based on network load balancing and spatial-temporal graph convolution network is constructed. Moreover, through simulation, the performance of this model is analyzed from perspectives of Accuracy, Precision, Recall, and F1-score. The experimental results demonstrate that in comparative analysis, the accuracy of road network prediction of the model reported here is 92.70%, which is at least 2.92% higher than that of the models proposed by other scholars. Through the analysis of the security performance of network data transmission, it is found that this model achieves a lower average delay time than other comparative models. Besides, the message delivery rate is basically stable at 80%, and the message leakage rate is basically stable at about 10%. Therefore, the prediction model for autonomous cars constructed here not only ensures low delay but also has excellent network security performance, so that information can interact more efficiently. The research outcome can provide an experimental basis for intelligent development and safety performance improvement in the transportation field of smart cities. © 2022 The Authors
|
|
| 16. |
- Nalpantidis, Lazaros, et al.
(författare)
-
Obtaining reliable depth maps for robotic applications from a quad-camera system
- 2009
-
Ingår i: INTELLIGENT ROBOTICS AND APPLICATIONS, PROCEEDINGS. - Berlin : Springer Berlin/Heidelberg. - 9783642108167 ; , s. 906-916
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous navigation behaviors in robotics often require reliable depth maps. The use of vision sensors is the most popular choice in such tasks. On the other hand, accurate vision-based depth computing methods suffer from long execution times. This paper proposes a novel quad-camera based system able to calculate fast and accurately a single depth map of a scenery. The four cameras are placed on the corners of a square. Thus, three, differently oriented, stereo pairs result when considering a single reference image (namely an horizontal, a vertical and a diagonal pair). The proposed system utilizes a custom tailored, simple, rapidly executed stereo correspondence algorithm applied to each stereo pair. This way, the computational load is kept within reasonable limits. A reliability measure is used in order to validate each point of the resulting disparity maps. Finally, the three disparity maps are fused together according to their reliabilities. The maximum reliability is chosen for every pixel. The final output of the proposed system is a highly reliable depth map which can be used for higher level robotic behaviors.
|
|
| 17. |
-
Imaging and sensing for unmanned aircraft systems Volume 2: Deployment and applications
- 2020
-
Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- This two volume book set explores how sensors and computer vision technologies are used for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic re-planning and reconfiguration of unmanned aircraft systems (UAS). Volume 1 concentrates on UAS control and performance methodologies including Computer Vision and Data Storage, Integrated Optical Flow for Detection and Avoidance Systems, Navigation and Intelligence, Modeling and Simulation, Multisensor Data Fusion, Vision in Micro-Aerial Vehicles (MAVs), Computer Vision in UAV using ROS, Security Aspects of UAV and Robot Operating System, Vision in Indoor and Outdoor Drones, Sensors and Computer Vision, and Small UAVP for Persistent Surveillance. Volume 2 focuses on UAS deployment and applications including UAV-CPSs as a Testbed for New Technologies and a Primer to Industry 5.0, Human-Machine Interface Design, Open Source Software (OSS) and Hardware (OSH), Image Transmission in MIMO-OSTBC System, Image Database, Communications Requirements, Video Streaming, and Communications Links, Multispectral vs Hyperspectral Imaging, Aerial Imaging and Reconstruction of Infrastructures, Deep Learning as an Alternative to Super Resolution Imaging, and Quality of Experience (QoE) and Quality of Service (QoS).
|
|
| 18. |
- Kavathatzopoulos, Iordanis, 1956-, et al.
(författare)
-
How ethical robots process information, communicate and act
- 2015
-
Ingår i: 1st TRANSOR Workshop.
-
Konferensbidrag (refereegranskat)abstract
- Robots can be of great help to obtain optimal solutions to problems in situations where humans have difficulties to perceive and process information, or make decisions and implement actions because of the quantity, variation and complexity of information. However, if they do not act in accordance to our ethical values they will not be used or will cause harm. Classical philosophical theory and psychological research on problem solving and decision making gives us a concrete definition of ethics and opens up the way for the construction of robots that can support handling of moral problems. Linguistic research focusing on language use as realization of meaning during the communication between humans and robots gives us the tools for investigating how particular linguistic features such as words and grammar may be related to ethical thinking. In such research work we can focus on three different kinds of robots: The first one is already programmed to act in certain ways, and the focus is on designers using ethical tools to identify moral problems and formulate solutions. The second is an integrated system which is also pre-programmed but also contains an ethical tool to gather information, to present it to the operators and to communicate with them. The third is trained autonomous systems in which we will implement automatic judgment. Such research will help us to clarify theoretical issues, to formulate working methods, and to develop technical solutions that will support ethical decision making of automated IT systems.
|
|
| 19. |
- Ambrus, Rares
(författare)
-
Unsupervised construction of 4D semantic maps in a long-term autonomy scenario
- 2017
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robots are operating for longer times and collecting much more data than just a few years ago. In this setting we are interested in exploring ways of modeling the environment, segmenting out areas of interest and keeping track of the segmentations over time, with the purpose of building 4D models (i.e. space and time) of the relevant parts of the environment.Our approach relies on repeatedly observing the environment and creating local maps at specific locations. The first question we address is how to choose where to build these local maps. Traditionally, an operator defines a set of waypoints on a pre-built map of the environment which the robot visits autonomously. Instead, we propose a method to automatically extract semantically meaningful regions from a point cloud representation of the environment. The resulting segmentation is purely geometric, and in the context of mobile robots operating in human environments, the semantic label associated with each segment (i.e. kitchen, office) can be of interest for a variety of applications. We therefore also look at how to obtain per-pixel semantic labels given the geometric segmentation, by fusing probabilistic distributions over scene and object types in a Conditional Random Field.For most robotic systems, the elements of interest in the environment are the ones which exhibit some dynamic properties (such as people, chairs, cups, etc.), and the ability to detect and segment such elements provides a very useful initial segmentation of the scene. We propose a method to iteratively build a static map from observations of the same scene acquired at different points in time. Dynamic elements are obtained by computing the difference between the static map and new observations. We address the problem of clustering together dynamic elements which correspond to the same physical object, observed at different points in time and in significantly different circumstances. To address some of the inherent limitations in the sensors used, we autonomously plan, navigate around and obtain additional views of the segmented dynamic elements. We look at methods of fusing the additional data and we show that both a combined point cloud model and a fused mesh representation can be used to more robustly recognize the dynamic object in future observations. In the case of the mesh representation, we also show how a Convolutional Neural Network can be trained for recognition by using mesh renderings.Finally, we present a number of methods to analyse the data acquired by the mobile robot autonomously and over extended time periods. First, we look at how the dynamic segmentations can be used to derive a probabilistic prior which can be used in the mapping process to further improve and reinforce the segmentation accuracy. We also investigate how to leverage spatial-temporal constraints in order to cluster dynamic elements observed at different points in time and under different circumstances. We show that by making a few simple assumptions we can increase the clustering accuracy even when the object appearance varies significantly between observations. The result of the clustering is a spatial-temporal footprint of the dynamic object, defining an area where the object is likely to be observed spatially as well as a set of time stamps corresponding to when the object was previously observed. Using this data, predictive models can be created and used to infer future times when the object is more likely to be observed. In an object search scenario, this model can be used to decrease the search time when looking for specific objects.
|
|
| 20. |
- Kostavelis, Ioannis, et al.
(författare)
-
Supervised traversability learning for robot navigation
- 2011
-
Ingår i: 12th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2011. - Berlin, Heidelberg : Springer Berlin/Heidelberg. ; , s. 289-298
-
Konferensbidrag (refereegranskat)abstract
- This work presents a machine learning method for terrain's traversability classification. Stereo vision is used to provide the depth map of the scene. Then, a v-disparity image calculation and processing step extracts suitable features about the scene's characteristics. The resulting data are used as input for the training of a support vector machine (SVM). The evaluation of the traversability classification is performed with a leave-one-out cross validation procedure applied on a test image data set. This data set includes manually labeled traversable and non-traversable scenes. The proposed method is able to classify the scene of further stereo image pairs as traversable or non-traversable, which is often the first step towards more advanced autonomous robot navigation behaviours.
|
|
