Autonomous localization and mapping based on biomimetic sonar in natural environments

Abstract

Bats navigating in dense vegetation based on biosonar have to obtain the necessary sensory information from “clutter echoes,” i.e., echoes that are superpositions of contributions of many reflecting facets (e.g., leaves). Since the locations and reflective properties of the individual facets are unknown, clutter echoes have to be treated as random signals that can neither be predicted nor—under most practical circumstance—be replicated. Nevertheless, prior research has shown that deep neural networks are capable of extracting fairly precise location information from clutter echoes. This raises the question whether clutter echoes could be used to provide navigational guidance in a conventional simultaneous localization and mapping (SLAM) framework, which is to a large extent dependent on precise and deterministic measurement to generate a localized map. Our hypothesis is that biomimetic sonar echoes indeed contain the information that is necessary support to local path planning, which can be utilized by a suitable deep learning architecture and training process. To investigate this issue, we have collected data in natural, heavily vegetated environments using a biomimetic sonar head that mimics the periphery of the biosonar system in horseshoe bats. The annotation of the data and the network training process is currently undergoing.