Japan Advanced Institute of Science and Technology

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    15493 research outputs found

    Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications

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    This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks. The SF relay extracts and transmits the semantic features, which reduces forwarding payload, and also improves the network robustness against intralink errors. Based on the theoretical basis for cooperative communications with side information and the turbo principle, we design a joint source-channel coding algorithm to iteratively exchange the extrinsic information for enhancing the decoding gains at the destination. Surprisingly, simulation results indicate that even in bad channel conditions, SF relaying can still effectively improve the recovered information quality

    Object-Oriented Semantic Mapping for Reliable UAVs Navigation

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    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-theart 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 BoTSORT 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.The 12th International Conference on Control, Automation and Information Sciences (ICCAIS 2023), November 27-29, 2023, Hanoi, Vietna

    S3M: Semantic Segmentation Sparse Mapping for UAVs with RGB-D Camera

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    Unmanned Aerial Vehicles (UAVs) hold immense potential for critical applications, such as search and rescue operations, where accurate perception of indoor environments is paramount. However, the concurrent amalgamation of localization, 3D reconstruction, and semantic segmentation presents a notable hurdle, especially in the context of UAVs equipped with constrained power and computational resources. This paper presents a novel approach to address challenges in semantic information extraction and utilization within UAV operations. Our system integrates state-of-the-art visual SLAM to estimate a comprehensive 6-DoF pose and advanced object segmentation methods at the back end. To improve the computational and storage efficiency of the framework, we adopt a streamlined voxel-based 3D map representation - OctoMap to build a working system. Furthermore, the fusion algorithm is incorporated to obtain the semantic information of each frame from the front-end SLAM task, and the corresponding point. By leveraging semantic information, our framework enhances the UAV’s ability to perceive and navigate through indoor spaces, addressing challenges in pose estimation accuracy and uncertainty reduction. Through Gazebo simulations, we validate the efficacy of our proposed system and successfully embed our approach into a Jetson Xavier AGX unit for real-world applications.2024 IEEE/SICE International Symposium on System Integration (SII), Ha Long, Vietnam, January 8-11, 202


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    Supervisor: 岡田 将吾先端科学技術研究科修士(情報科学

    Zero-resourced Speech-to-Speech Translation for Unpaired and Untranscribed Languages Based on Visually Grounded Self-Supervised Speech Models

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    Supervisor: SAKTI Sakriani Watiasri先端科学技術研究科修士(情報科学


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    Supervisor: 長谷川 忍先端科学技術研究科修士(情報科学


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    Supervisor: 金井 秀明先端科学技術研究科修士(知識科学


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    Supervisor: 山口 政之先端科学技術研究科博


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    Supervisor: 長谷川 忍先端科学技術研究科修士(情報科学


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