Predicting LiDAR Data From Sonar Images

Niels Balemans,Jan Steckel,Peter Hellinckx

doi:10.1109/access.2021.3072551

Niels Balemans, Jan Steckel + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3072551

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Abstract

Sensors using ultrasonic sound have proven to provide accurate 3D perception in difficult environments where other modalities fail. Several industrial sectors need accurate and reliable sensing in these harsh conditions. The conventional LiDAR/camera approach in many state-of-the-art autonomous navigation methods is limited to environments with optimal sensing conditions for visual modalities. The use of other sensing modalities can thus improve reliability and usability and increase the application potential of autonomous agents. Ultrasonic measurements provide, compared to LiDAR, a much sparser representation of the environment, making a direct replacement of the LiDAR sensor difficult. In this work, we propose a method to predict LiDAR point cloud data from an in-air acoustic sonar sensor using a convolutional stacked autoencoder. This provides a robotic system with high-resolution measurements and allows for easier integration into existing systems to safely navigate environments where visual modalities become unreliable and less accurate. A video of our predictions is available at https://youtu.be/jlx1S-tslmo .

Highlights

Autonomous robotics have proven to be tremendously useful for many applications in several sectors, going from manufacturing [1] over predictive maintenance [2], [3] to security and surveillance [4], [5]
For the autonomous navigation of robotic systems, countless methodologies and novel approaches can be found in order to improve sensor measurement quality and environment understanding [6], [7]
We argue that the eRTIS in-air sonar sensor [12] is an excellent addition to multimodal sensing systems that should be capable of accurately sensing in difficult and harsh environments

Summary

Introduction

Autonomous robotics have proven to be tremendously useful for many applications in several sectors, going from manufacturing [1] over predictive maintenance [2], [3] to security and surveillance [4], [5]. We interpreted the problem as an inverse imagining problem where the goal is to reconstruct LiDAR data using the measurements of the eRTIS sonar sensor developed by our research group [12].

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Conclusion