arXiv 2401.081322024-01-16cs.RO

面向可靠无人机导航的面向对象语义建图

Object-Oriented Semantic Mapping for Reliable UAVs Navigation

University of Engineering and Technology, Vietnam National University Hanoi（越南国立大学河内工程技术大学）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）

Thanh Nguyen Canh, Armagan Elibol, Nak Young Chong, Xiem HoangVan

备注：: In the 12th International Conference on Control, Automation and Information Sciences (ICCAIS 2023), Hanoi, Vietnam

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AI总结：

本文提出一种结合YOLOv8检测、BoT-SORT跟踪与CartoGrapher SLAM的语义建图系统，为无人机生成包含物体类别、位置和高度的概率地图，以提升真实环境中的导航可靠性。

摘要：

为了在真实环境中自主导航，尤其是在搜索与救援任务中，无人机（UAV）需要全面的地图以确保安全。然而，主流的度量地图往往缺乏对整体场景理解至关重要的语义信息。本文提出了一种系统，利用RGB-D图像构建概率度量地图，并融入从环境中提取的物体信息。我们的方法在前端结合了最先进的基于YOLOv8的物体检测框架，在后端结合了2D SLAM方法——CartoGrapher。为了有效跟踪和定位从前端接口提取的语义物体类别，我们采用了创新的BoT-SORT方法。我们引入了一种新颖的关联方法，用于提取物体位置，并将其投影到度量地图上。与以往研究不同，我们的方法考虑了在包含各种中空底部物体的环境中进行可靠导航。我们系统的输出是一张概率地图，通过融入物体特定属性（包括类别区分、精确定位和物体高度），显著增强了地图的表示能力。我们进行了多项实验来评估所提出的方法。结果表明，机器人能够有效生成包含多个物体（尤其是椅子和桌子）的增强语义地图。此外，我们的系统在嵌入式计算机——Jetson Xavier AGX单元上进行了评估，以展示其在真实世界应用中的使用案例。

英文摘要：

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.

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