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：各种自主应用依赖于识别其环境中的特定已知地标。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许多常见任务奠定了基础。这需要根据容易累积误差的感官输入实时构建地图。识别环境中的地标在纠正这些误差及进一步提高SLAM精度方面起着至关重要的作用。目前，执行SLAM最常用的传感器是光学传感器，如摄像头或激光雷达传感器。这些传感器通常需要QR码等地标作为前提条件。然而，在雾天、粉尘、反光或玻璃富集环境等特定条件下，此类传感器会变得不可靠。声纳已被证明是更好应对此类情况的可行替代方案。然而，声学传感器也需要不同类型的地标。本文提出了一种利用嵌入式的实时成像声纳，基于反射声学回波的频带训练支持向量机，以检测仿生声学地标存在的方法。