Armağan Elibol

Japan Advanced Institute of Science and Technology

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Nak Young Chong

Japan Advanced Institute of Science and Technology

Rafael García

University of Girona

Nuno Gracias

University of Girona

Ziyan Gao

Japan Advanced Institute of Science and Technology

Jinwhan Kim

Korea Advanced Institute of Science and Technology

László Neumann

Institució Catalana de Recerca i Estudis Avançats

Tudor Nicosevici

University of Girona

J. Escartı́n

Laboratoire de Géologie de l’École Normale Supérieure

AŞ

Ayla Şaylı

Yıldız Technical University

Xiem HoangVan

Vietnam National University, Hanoi

Cooperative Institutions

Park University

673

Chinese Academy of Sciences

Zhejiang University

Peking University

University of Chinese Academy of Sciences

Japan Advanced Institute of Science and Technology

Central South University

University of Girona

Huazhong University of Science and Technology

Magna Graecia University

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Object-Oriented Semantic Mapping for Reliable UAVs Navigation

arXiv (Cornell University) (2024)

Thanh Nguyen Canh Armağan Elibol Nak Young Chong Xiem HoangVan

To autonomously navigate in real-world environments, special in search and rescue operations, Unmanned Aerial Vehicles (UAVs) necessitate comprehensive maps to ensure safety. However, the prevalent metric map often lacks semantic information crucial for holistic scene comprehension. In this paper, we proposed a system to construct a probabilistic metric map enriched with object information extracted from the environment from RGB-D images. Our approach combines a state-of-the-art YOLOv8-based object detection framework at the front end and a 2D SLAM method - CartoGrapher at the back end. To effectively track and position semantic object classes extracted from the front-end interface, we employ the innovative BoT-SORT methodology. A novel association method is introduced to extract the position of objects and then project it with the metric map. Unlike previous research, our approach takes into reliable navigating in the environment with various hollow bottom objects. The output of our system is a probabilistic map, which significantly enhances the map's representation by incorporating object-specific attributes, encompassing class distinctions, accurate positioning, and object heights. A number of experiments have been conducted to evaluate our proposed approach. The results show that the robot can effectively produce augmented semantic maps containing several objects (notably chairs and desks). Furthermore, our system is evaluated within an embedded computer - Jetson Xavier AGX unit to demonstrate the use case in real-world applications.

Semantic mapping

Representation

Interface (matter)

10.48550/arxiv.2401.08132

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Fast Underwater Image Mosaicing through Submapping

Journal of Intelligent & Robotic Systems (2016)

Armağan Elibol Jinwhan Kim Nuno Gracias Rafael García

Feature (linguistics)

Similarity (geometry)

Representation

Hierarchical clustering

10.1007/s10846-016-0380-x

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Citations (8)

A Two-Step Global Alignment Method for Feature-Based Image Mosaicing

Mathematical and Computational Applications (2016)

Armağan Elibol

Image mosaicing sits at the core of many optical mapping applications with mobile robotic platforms. As these platforms have been evolving rapidly and increasing their capabilities, the amount of data they are able to collect is increasing drastically. For this reason, the necessity for efficient methods to handle and process such big data has been rising from different scientific fields, where the optical data provides valuable information. One of the challenging steps of image mosaicing is finding the best image-to-map (or mosaic) motion (represented as a planar transformation) for each image while considering the constraints imposed by inter-image motions. This problem is referred to as Global Alignment (GA) or Global Registration, which usually requires a non-linear minimization. In this paper, following the aforementioned motivations, we propose a two-step global alignment method to obtain globally coherent mosaics with less computational cost and time. It firstly tries to estimate the scale and rotation parameters and then the translation parameters. Although it requires a non-linear minimization, Jacobians are simple to compute and do not contain the positions of correspondences. This allows for saving computational cost and time. It can be also used as a fast way to obtain an initial estimate for further usage in the Symmetric Transfer Error Minimization (STEMin) approach. We presented experimental and comparative results on different datasets obtained by robotic platforms for mapping purposes.

Minification

Image registration

Feature (linguistics)

Image translation

10.3390/mca21030030

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Attention-model Guided Image Enhancement for Robotic Vision Applications

Ming Yi Wanxiang Li Armağan Elibol Nak Young Chong

Optical data is one of the crucial information resources for robotic platforms to sense and interact with the environment being employed. Obtained image quality is the main factor of having a successful application of sophisticated methods (e.g., object detection and recognition). In this paper, a method is proposed to improve the image quality by enhancing the lighting and denoising. The proposed method is based on a generative adversarial network (GAN) structure. It makes use of the attention model both to guide the enhancement process and to apply denoising simultaneously thanks to the step of adding noise on the input of discriminator networks. Detailed experimental and comparative results using real datasets were presented in order to underline the performance of the proposed method.

Discriminator

10.1109/ur49135.2020.9144966

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Citations (5)

Toward Computationally Efficient Path Generation and Push Planning for Robotic Nonprehensile Manipulation

Armağan Elibol Nak Young Chong

Robots have been involved in our daily life more and more. This involvement comes along with a necessity for endowing robots to interact with real-world objects that are novel to them. Among many others, pushing is one of the important tasks for robotic arms (or manipulators) as it has been widely used in the pipeline of the more complicated tasks (e.g., grasping, singulation, reorienting, and similar other tasks.), In this paper, we present simple yet efficient path generation and push planning algorithms for pushing an object from a given point to a target point in the given grid map with static obstacles. Our path generation module aims to generate waypoints taking into account obstacles and quantizing them into circular regions in order to reduce the computational cost. The path is formed using the waypoints by minimizing the designed cost function. We also define a cost function for planning pushing steps aiming to both minimize the number of pushing actions and deviation from the path generated. We present comparative numerical results using simulated data. Our preliminary findings show promising results leaving room for further improvements.

10.1109/icara56516.2023.10125726

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Topology Estimation Using Bundle Adjustment

Springer tracts in advanced robotics (2012)

Armağan Elibol Nuno Gracias Rafael García

Similarity (geometry)

Bundle adjustment

Tree (set theory)

10.1007/978-3-642-30313-5_5

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Object-Oriented Semantic Mapping for Reliable UAVs Navigation

2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS) (2023)

Thanh Nguyen Canh Armağan Elibol Nak Young Chong Xiem HoangVan

Semantic mapping

Representation

Interface (matter)

10.1109/iccais59597.2023.10382351

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Citations (3)

A GAN-based Approach to Communicative Gesture Generation for Social Robots

Nguyen Tan Viet Tuyen Armağan Elibol Nak Young Chong

People use a wide range of non-verbal behaviors to signal their intentions in interpersonal relationships. Being echoed by the proven benefits and impact of people's social interaction skills, considerable attention has been paid to generating non-verbal cues for social robots. In particular, communicative gestures help social robots emphasize the thoughts in their speech, describing something or conveying their feelings using bodily movements. This paper introduces a generative framework for producing communicative gestures to better enforce the semantic contents that social robots express. The proposed model is inspired by the Conditional Generative Adversarial Network and built upon a convolutional neural network. The experimental results confirmed that a variety of motions could be generated for expressing input contexts. The framework can produce synthetic actions defined in a high number of upper body joints, allowing social robots to clearly express sophisticated contexts. Indeed, the fully implemented model shows better performance than the one without Action Encoder and Decoder. Finally, the generated motions were transformed into the target robot and combined with the robot's speech, with an expectation of gaining broad social acceptance.

10.1109/arso51874.2021.9542828

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Citations (6)

Enabling landings on irregular surfaces for unmanned aerial vehicles via a novel robotic landing gear

Intelligent Service Robotics (2022)

TsungHsuan Huang Armağan Elibol Nak Young Chong

Landing gear

Drone

10.1007/s11370-022-00420-y

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Citations (5)

Persona-based Dialogue Generation with Sentence Embedding for Prolonged Human-Robot Communication

Yue Chen Armağan Elibol Nak Young Chong

It is one of the most fundamental and challenging problems for current dialogue generation systems to maintain the consistency of dialogue logic during the conversation. Also, the lack of an open-source annotated persona-based dialogue dataset may lead to insufficient training volume for the model. Besides, the computational time and computational memory required by the attention mechanism have become drastically large. In order to overcome these problems, in this work, we propose a vertical-structure model based on the BERT model with the sentence embedding method. The model generates a raw response based on the sentence embeddings of context and persona and finally revises the raw response according to the persona. Moreover, an understanding task is designed for the BERT decoder to have a better revision ability. Considering the difference between the generation and the understanding models, three kinds of input methods are designed for each part of the model. Comparative and experimental results are presented using publicly available datasets.

Persona

10.23919/iccas59377.2023.10316939

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