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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18808

Title: S3M: Semantic Segmentation Sparse Mapping for UAVs with RGB-D Camera
Authors: Canh, Thanh Nguyen
Nguyen, Van-Truong
Van, Xiem Hoang
Elibol, Armagan
Chong, Nak Young
Keywords: Semantic Mapping
Issue Date: 2024-01-08
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2024 IEEE/SICE International Symposium on System Integration (SII)
Start page: 899
End page: 905
DOI: 10.1109/SII58957.2024.10417379
Abstract: 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.
Rights: This is the author's version of the work. Copyright (C) 2024 IEEE. 2024 IEEE/SICE International Symposium on System Integration (SII), Ha Long, Vietnam, 2024, pp. 899-905, DOI: 10.1109/SII58957.2024.10417379. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/18808
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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