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Dmitry Yudin - IEEE Xplore Author Profile
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Dmitry Yudin

Also published under: Dmitry A. Yudin

Publications
14
Citations
87
Publications by Year
20142025
Co-Authors:
Show All Co-Authors (31)
Image of author Dmitry Yudin

Dmitry Yudin

Also published under: Dmitry A. Yudin

Affiliation

MIPT, Dolgoprudny, Russia
AIRI, Moscow, Russia

Publication Topics

Biography

Dmitry A. Yudin was born in Belgorod, Russia, in 1988. He received the Engineering Diploma degree in automation of technological processes and production, in 2010, and the Ph.D. degree in computer science from the Belgorod State Technological University (BSTU named after V. G. Shukhov), Belgorod, in 2014.,From 2009 to 2019, he was a Researcher and an Assistant Professor with the Technical Cybernetics Department, BSTU. Since 2019, he has been the Head of the Intelligent Transport Laboratory, Moscow Institute of Physics and Technology, Moscow, Russia. Since 2021, he has been a Senior Researcher with the Artificial Intelligence Research Institute (AIRI), Moscow. He is the author more than 100 articles. His research interests include computer v...
Publications
14
Citations
87
Publications by Year
20142025
Co-Authors:
Show All Co-Authors (31)
Author's Published Works

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PRISM-TopoMap: Online Topological Mapping With Place Recognition and Scan Matching

Kirill Muravyev;Alexander Melekhin;Dmitry Yudin;Konstantin Yakovlev

IEEE Robotics and Automation Letters
Year: 2025 | Volume: 10, Issue: 4 | Journal Article |
Mapping is one of the crucial tasks enabling autonomous navigation of a mobile robot. Conventional mapping methods output a dense geometric map representation, e.g. an occupancy grid, which is not trivial to keep consistent for prolonged runs covering large environments. Meanwhile, capturing the topological structure of the workspace enables fast path planning, is typically less prone to odometry ...Show More

PRISM-TopoMap: Online Topological Mapping With Place Recognition and Scan Matching

Kirill Muravyev;Alexander Melekhin;Dmitry Yudin;Konstantin Yakovlev

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

Fine-Tuning Multimodal Transformer Models for Generating Actions in Virtual and Real Environments

Aleksei Staroverov;Andrey S. Gorodetsky;Andrei S. Krishtopik;Uliana A. Izmesteva;Dmitry A. Yudin;Alexey K. Kovalev;Aleksandr I. Panov

IEEE Access
Year: 2023 | Volume: 11 | Journal Article |
Cited by: Papers (1)
In this work, we propose and investigate an original approach to using a pre-trained multimodal transformer of a specialized architecture for controlling a robotic agent in an object manipulation task based on language instruction, which we refer to as RozumFormer. Our model is based on a bimodal (text-image) transformer architecture originally trained for solving tasks that use one or both modali...Show More

Fine-Tuning Multimodal Transformer Models for Generating Actions in Virtual and Real Environments

Aleksei Staroverov;Andrey S. Gorodetsky;Andrei S. Krishtopik;Uliana A. Izmesteva;Dmitry A. Yudin;Alexey K. Kovalev;Aleksandr I. Panov

Year: 2023 | Volume: 11 | Journal Article |

DAPS3D: Domain Adaptive Projective Segmentation of 3D LiDAR Point Clouds

Alexey A. Klokov;Di Un Pak;Aleksandr Khorin;Dmitry A. Yudin;Leon Kochiev;Vladimir D. Luchinskiy;Vitaly D. Bezuglyj

IEEE Access
Year: 2023 | Volume: 11 | Journal Article |
Cited by: Papers (2)
LiDARs are one of the key sources of reliable environmental ranging information for autonomous vehicles. However, segmentation of 3D scene elements (roads, buildings, people, cars, etc.) based on LiDAR point clouds has limitations. On the one hand, point- and voxel-based segmentation neural networks do not offer sufficiently high speed. On the other hand, modern labeled datasets primarily consist ...Show More

DAPS3D: Domain Adaptive Projective Segmentation of 3D LiDAR Point Clouds

Alexey A. Klokov;Di Un Pak;Aleksandr Khorin;Dmitry A. Yudin;Leon Kochiev;Vladimir D. Luchinskiy;Vitaly D. Bezuglyj

Year: 2023 | Volume: 11 | Journal Article |

Vector Symbolic Scene Representation for Semantic Place Recognition

Daniil Kirilenko;Alexey K. Kovalev;Yaroslav Solomentsev;Alexander Melekhin;Dmitry A. Yudin;Aleksandr I. Panov

2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Most state-of-the-art methods do not explicitly use scene semantics for place recognition by the images. We address this problem and propose a new two-stage approach referred to as TSVLoc. It solves the place recognition task as the image retrieval problem and enriches any well-known method. In the first model-agnostic stage, any modern neural network model that does not directly use semantics, e....Show More

Vector Symbolic Scene Representation for Semantic Place Recognition

Daniil Kirilenko;Alexey K. Kovalev;Yaroslav Solomentsev;Alexander Melekhin;Dmitry A. Yudin;Aleksandr I. Panov

Year: 2022 | Conference Paper |

FMFNet: Improve the 3D Object Detection and Tracking via Feature Map Flow

Youshaa Murhij;Dmitry Yudin

2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Three-dimensional object detection and tracking from point clouds are important computer vision tasks for robots and vehicles where objects can be represented as 3D boxes. Improving the accuracy of understanding the environment is critical for successful autonomous driving. This paper presents a simple yet efficient method called “Feature Map Flow, FMF” for 3D object detection and tracking, consid...Show More

FMFNet: Improve the 3D Object Detection and Tracking via Feature Map Flow

Youshaa Murhij;Dmitry Yudin

Year: 2022 | Conference Paper |

Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images

Ilya Shepel;Vasily Adeshkin;Ilya Belkin;Dmitry A. Yudin

IEEE Transactions on Intelligent Transportation Systems
Year: 2022 | Volume: 23, Issue: 9 | Journal Article |
Cited by: Papers (14)
The detection of dynamic and static obstacles is a key task for the navigation of autonomous ground vehicles. The article presents a new algorithm for generating an occupancy map of the surrounding space from noisy point clouds obtained from one or several stereo cameras. The camera images are segmented by the proposed deep neural network FCN-ResNet-M-OC, which combines the speed of the FCN-ResNet...Show More

Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images

Ilya Shepel;Vasily Adeshkin;Ilya Belkin;Dmitry A. Yudin

Year: 2022 | Volume: 23, Issue: 9 | Journal Article |

Real-Time Object Navigation With Deep Neural Networks and Hierarchical Reinforcement Learning

Aleksey Staroverov;Dmitry A. Yudin;Ilya Belkin;Vasily Adeshkin;Yaroslav K. Solomentsev;Aleksandr I. Panov

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (30)
In the last years, deep learning and reinforcement learning methods have significantly improved mobile robots in such fields as perception, navigation, and planning. But there are still gaps in applying these methods to real robots due to the low computational efficiency of recent neural network architectures and their poor adaptability to robotic experiments' realities. In this article, we consid...Show More

Real-Time Object Navigation With Deep Neural Networks and Hierarchical Reinforcement Learning

Aleksey Staroverov;Dmitry A. Yudin;Ilya Belkin;Vasily Adeshkin;Yaroslav K. Solomentsev;Aleksandr I. Panov

Year: 2020 | Volume: 8 | Journal Article |

Detection of Big Animals on Images with Road Scenes using Deep Learning

Dmitry Yudin;Anton Sotnikov;Andrey Krishtopik

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)
Year: 2019 | Conference Paper |
Cited by: Papers (10)
The recognition of big animals on the images with road scenes has received little attention in modern research. There are very few specialized data sets for this task. Popular open data sets contain many images of big animals, but the most part of them is not correspond to road scenes that is necessary for on-board vision systems of unmanned vehicles. The paper describes the preparation of such a ...Show More

Detection of Big Animals on Images with Road Scenes using Deep Learning

Dmitry Yudin;Anton Sotnikov;Andrey Krishtopik

Year: 2019 | Conference Paper |

Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm

Ilya Belkin;Sergey Tkachenko;Dmitry Yudin

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
The paper analyzes data sets containing images with labeled traffic signs, as well as modern approaches for their detection and classification on images of urban scenes. Particular attention is paid to the recognition of Russian types of traffic signs. Various modern architectures of deep neural networks for the simultaneous object detection and classification were studied, including Faster R-CNN,...Show More

Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm

Ilya Belkin;Sergey Tkachenko;Dmitry Yudin

Year: 2019 | Conference Paper |

Usage of fully convolutional network with clustering for traffic light detection

Dmitry Yudin;Dmitry Slavioglo

2018 7th Mediterranean Conference on Embedded Computing (MECO)
Year: 2018 | Conference Paper |
Cited by: Papers (13)
In this paper we consider a traffic light detector constructed on the basis of a fully convolutional neural network for segmenting traffic lights on image and subsequent clustering, which allows us to obtain bounding boxes for traffic lights. The proposed approach is compared with one of the most effective object detectors - Single Shot Multibox detector (SSD). We implemented algorithms for object...Show More

Usage of fully convolutional network with clustering for traffic light detection

Dmitry Yudin;Dmitry Slavioglo

Year: 2018 | Conference Paper |

Vehicle recognition and its trajectory registration on the image sequence using deep convolutional neural network

Dmitry Yudin;Alexander Knysh

2017 International Conference on Information and Digital Technologies (IDT)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
The article shows the methods of vehicle recognition on the image sequence and its trajectory registration. As a recognition algorithm authors used Viola-Jones method with optical flow filter and the deep convolutional neural network in combination with sliding window technique for vehicle detection task. Also authors analyze approaches to registration of detected vehicle trajectories on image seq...Show More

Vehicle recognition and its trajectory registration on the image sequence using deep convolutional neural network

Dmitry Yudin;Alexander Knysh

Year: 2017 | Conference Paper |

Control system of robotic complex for constructions and buildings printing

Alexander Bazhanov;Dmitry Yudin;Vasily Porkhalo;Evgeny Karikov

2016 International Conference on Information and Digital Technologies (IDT)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
This article describes the development of control system of robotic complex for constructions and buildings printing. Based on analysis of existing approaches authors propose the solution for control system development for 3D-printer with gantry-type construction, which can move on rails. Fully functional model of the control system was created by authors. It includes graph in the form of Petri ne...Show More

Control system of robotic complex for constructions and buildings printing

Alexander Bazhanov;Dmitry Yudin;Vasily Porkhalo;Evgeny Karikov

Year: 2016 | Conference Paper |

Statistical methods analysis of green control of complex energy-intensive object

Dmitry Yudin;Sergey Frolov;Valery Magergut;Vasiliy Rubanov

2015 International Conference on Information and Digital Technologies
Year: 2015 | Conference Paper |
The article describes the task of green control of the energy-intensive object on the example of the rotary cement kiln. It represents the control system model on basis of firing process statistical data, obtained by conventional sensors and vision system. To determine the "input-output" correlation three methods are applied: the classical regression model, group method of data handling and neuro-...Show More

Statistical methods analysis of green control of complex energy-intensive object

Dmitry Yudin;Sergey Frolov;Valery Magergut;Vasiliy Rubanov

Year: 2015 | Conference Paper |

Fuzzy control of rotary cement kiln using sintering zone image recognition

Dmitry Yudin;Valery Magergut;Vasiliy Rubanov

The 10th International Conference on Digital Technologies 2014
Year: 2014 | Conference Paper |
Cited by: Papers (2)
Article analyses modern machine vision-based approaches of the rotary kiln monitoring and control which reduces energy consumption and improve clinker quality. Article describes fuzzy advising control unit developed by the authors. It based on sintering zone state assessment, the kiln rotation period and the relative change of the exhaust gases temperature. Advising control rule base for rotary ce...Show More

Fuzzy control of rotary cement kiln using sintering zone image recognition

Dmitry Yudin;Valery Magergut;Vasiliy Rubanov

Year: 2014 | Conference Paper |

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