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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18796

Title: Object-Oriented Semantic Mapping for Reliable UAVs Navigation
Authors: Canh, Thanh Nguyen
Elibol, Armagan
Chong, Nak Young
Van, Xiem Hoang
Keywords: Semantic mapping
Metric map
Issue Date: 2024-01-10
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: The 12th International Conference on Control, Automation and Information Sciences (ICCAIS 2023)
Start page: 139
End page: 144
DOI: 10.1109/ICCAIS59597.2023.10382351
Abstract: 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.
Rights: This is the author's version of the work. Copyright (C) 2023 IEEE. 2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2023, pp. 139-144. DOI: 10.1109/ICCAIS59597.2023.10382351. 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/10119/18796
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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