# Detecting and Classifying Bio-Inspired Artificial Landmarks Using In-Air 3D Sonar

arXiv: https://arxiv.org/abs/2308.05504 · HTML: https://mlsanity.com/paper/2308.05504/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [Towards In-Air Ultrasonic QR Codes: Deep Learning for Classification of Passive Reflector Constellations](https://mlsanity.com/paper/2509.06615/) (2025)

## Abstract (arXiv)

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.