使用空中3D声纳检测与分类仿生人工地标
Detecting and Classifying Bio-Inspired Artificial Landmarks Using In-Air 3D Sonar
AI总结：
本文提出利用嵌入式实时成像声纳和基于反射回波频带训练的支撑向量机，检测并分类仿生人工声学地标，以替代光学传感器在恶劣环境中不可靠的地标识别，提升SLAM精度。
摘要：
各种自主应用依赖于识别环境中特定的已知地标。例如，同时定位与建图（SLAM）是一项重要技术，为导航和长期目标跟踪等许多常见任务奠定了基础。这需要基于容易累积误差的传感输入实时构建地图。识别环境中的地标在纠正这些误差并进一步提高SLAM精度方面起着至关重要的作用。目前进行SLAM最常用的传感器是光学传感器，如相机或LiDAR传感器。这些传感器可以使用QR码等地标作为前提条件。然而，此类传感器在某些条件下变得不可靠，例如有雾、多尘、反射性强或玻璃丰富的环境。声纳已被证明是一种可行的替代方案，可以更好地应对此类情况。然而，声学传感器也需要不同类型的地标。在本文中，我们提出了一种方法，使用在反射声学回波的频带上训练的支撑向量机，并借助嵌入式实时成像声纳，来检测仿生声学地标的存在。
英文摘要：
Various autonomous applications rely on recognizing specific known landmarks in their environment. For example, Simultaneous Localization And Mapping (SLAM) is an important technique that lays the foundation for many common tasks, such as navigation and long-term object tracking. This entails building a map on the go based on sensory inputs which are prone to accumulating errors. Recognizing landmarks in the environment plays a vital role in correcting these errors and further improving the accuracy of SLAM. The most popular choice of sensors for conducting SLAM today is optical sensors such as cameras or LiDAR sensors. These can use landmarks such as QR codes as a prerequisite. However, such sensors become unreliable in certain conditions, e.g., foggy, dusty, reflective, or glass-rich environments. Sonar has proven to be a viable alternative to manage such situations better. However, acoustic sensors also require a different type of landmark. In this paper, we put forward a method to detect the presence of bio-mimetic acoustic landmarks using support vector machines trained on the frequency bands of the reflecting acoustic echoes using an embedded real-time imaging sonar.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
