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.

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

“…Under the umbrella of Artificial Intelligence (AI), these methods enable UUVs to delve deeper into the intricacies of underwater environments through sophisticated data processing. Deep Learning-based SLAM algorithms [78] empower UUVs with advanced cognitive abilities to make real-time decisions, adapt to dynamic underwater conditions, and navigate with unparalleled accuracy [39]. Deep Learning algorithms excel at extracting intricate patterns and representations from sensor data, encompassing visual, inertial, and acoustic inputs.…”

“…The accuracy and resilience of underwater SLAM systems through sensor fusion techniques encompasses vision-inertial SLAM, laser-vision SLAM, and multisensor SLAM [37,38]. Multisensor fusion is classified into data layer, feature layer, and decision layer fusion [39,40]. Visual SLAM faces challenges with low-quality images, while IMU-assisted sensors improve To enhance the accuracy and robustness of underwater SLAM systems, researchers often combine multiple sensors, leveraging sensor fusion techniques.…”

“…A novel crack assessment technique combines multisensor fusion SLAM and image superresolution [42]. A modality prediction approach is explored using LiDAR point cloud prediction from 3D acoustic ultrasonic sensor data [39]. Fusion SLAM algorithms also combine 2D LiDAR and RGB-D SLAM, offering a comprehensive visual representation [43].…”

SonoTraceLab—A Raytracing-Based Acoustic Modeling System for Simulating Echolocation Behavior of Bats

“…At this stage, SonoTraceLab supports a single emitter and multiple receivers, as this is a simulation setup which is applicable for many application scenarios, both in engineered sonar sensors [58], [59], [60], [61] as in biologically relevant echolocation setups, as both bats and dolphins can be approximated by a point-source with a known directivity [62]. The emitter and the receiver arrays are defined in the local sensor coordinate system [X s , Y s , Z s ], as shown in figure 1.…”