S2L-SLAM: Sensor Fusion Driven SLAM using Sonar, LiDAR and Deep Neural Networks

Abstract

The use of different modalities improves the perception of the environment in situations where the conventional sensors fail (camera and LiDAR). The inclusion of these modalities, such as sonar or radar, is however difficult as existing methods for the conventional sensors usually do not generalise well on these different environment representations. We experiment with a modality prediction method to keep using the existing methodologies and allow to separate the sensing system from the navigation stack of an autonomous agent. In previous work, we used a convolutional stacked autoencoder to predict LiDAR point cloud data using the data from our 3D in-air acoustic ultrasonic sensor (eRTIS). In this paper, we investigate the usability of the predicted data in off-the-shelf algorithms to safely navigate environments where visual modalities become unreliable and less accurate.