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.

翻译：各类自主应用依赖于识别环境中特定的已知地标。例如，同步定位与地图构建（SLAM）是一项重要技术，为导航和长期目标跟踪等常见任务奠定了基础。该技术需基于易累积误差的感知输入实时构建地图。识别环境中的地标对纠正这些误差、进一步提升SLAM精度具有关键作用。当前最常用的SLAM传感器是光学传感器，如相机或LiDAR传感器，这类传感器可借助QR码等地标作为前提条件。然而，此类传感器在雾天、多尘、反光或玻璃密集等特定环境下可靠性会下降。声纳已被证明是应对此类场景的更优替代方案。但声学传感器同样需要不同类型的地标。本文提出了一种利用嵌入式实时成像声纳，基于反射声学回波频带训练支持向量机，检测仿生声学地标存在性的方法。