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

TL;DR

This paper introduces a novel method for detecting bio-inspired artificial landmarks using in-air 3D sonar and machine learning, enhancing SLAM robustness in challenging environments where optical sensors fail.

Contribution

The paper presents a new approach to identify bio-mimetic acoustic landmarks with support vector machines trained on sonar echoes, addressing limitations of optical sensors in adverse conditions.

Findings

Effective detection of bio-inspired landmarks with real-time sonar

Improved SLAM robustness in foggy or dusty environments

Support vector machines accurately classify acoustic echoes

Abstract

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.