An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -- one of the main problems affecting other packages in underwater domain -- by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness

Surface reconstruction of ancient water storage systems an approach for sparse 3D sonar scans and fused stereo images

This work presents a process pipeline that addresses the problem of reconstructing surfaces of underwater structures from stereo images and sonar scans collected with a micro-ROV on the islands of Malta and Gozo. Using a limited sensor load, sonar and small GoPro Hero2 cameras, the micro-ROV is able to explore water systems and gather data. As a preprocess to the reconstruction pipeline, a 3D evidence grid is created by mosaicing horizontal and vertical sonar scans. A volumetric representation is then constructed using a level set method. Fine-scale details from the scene are captured in stereo cameras, and are transformed into point clouds and projected into the volume. A raycasting technique is used to trim the volume in accordance with the projected point clouds, thus reintroducing fine details to the rough sonar-generated model. The resulting volume is surfaced, yielding a final mesh which can be viewed and interacted with for archaeological and educational purposes. Initial results from both steps of the reconstruction pipeline are presented and discussed.peer-reviewe

Imaging beneath standing bodies of water in karst terrain

Side scan sonar and down scan sonar, sub bottom profiling, electrical resistivity tomography profiling (underwater cables), and continuous resistivity profiling (towed cable) surveys were conducted to characterize the lake sediments (rock and soil) beneath the man-made Little Prairie Lake, in central Missouri. Sub bottom profiling and electrical resistivity (with marine cables and towed cable) were used to determine variability in the lithology and thickness of sediments (soil and rock) beneath the lake, while side scan sonar was used to map the variations in the lithology/nature of exposed lakebed sediments and to locate the potential hazard of trees. Down scan sonar and sub bottom profiling were utilized to measure the water depth. On land, electrical resistivity tomography was used with multi-channel analysis of surface wave method to determine sediments, joints, and the depth of bedrock. Analyses of the acquired data revealed the location and orientation of the original stream channels (prior to the construction of the earth fill dam). The side scan sonar mapped the variations in the biomass at the bottom of the lake. Underwater electrical resistivity tomography and continuous resistivity profiling determined joints, sediments, and bedrock underneath water bodies. Using integrated marine geophysical tools help to evaluate the sub surface prior to any construction project (dam or bridge), are useful in determining the characteristics of lithology (fractured rock, intact rock and soil), and make it possible to map benthic habitat and the submerged potential hazards of trees on the lakebed as well as accurately measuring water depth --Abstract, page iii

3D virtualization of an underground semi-submerged cave system

Underwater caves represent the most challenging scenario for exploration, mapping and 3D modelling. In such complex environment, unsuitable to humans, highly specialized skills and expensive equipment are normally required. Technological progress and scientific innovation attempt, nowadays, to develop safer and more automatic approaches for the virtualization of these complex and not easily accessible environments, which constitute a unique natural, biological and cultural heritage. This paper presents a pilot study realised for the virtualization of 'Grotta Giusti' (Fig. 1), an underground semi-submerged cave system in central Italy. After an introduction on the virtualization process in the cultural heritage domain and a review of techniques and experiences for the virtualization of underground and submerged environments, the paper will focus on the employed virtualization techniques. In particular, the developed approach to simultaneously survey the semi-submersed areas of the cave relying on a stereo camera system and the virtualization of the virtual cave will be discussed

CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration

In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves. We address the problem of scarce annotated training data by preparing a comprehensive dataset for semantic segmentation of underwater cave scenes. It contains pixel annotations for important navigation markers (e.g. caveline, arrows), obstacles (e.g. ground plain and overhead layers), scuba divers, and open areas for servoing. Through comprehensive benchmark analyses on cave systems in USA, Mexico, and Spain locations, we demonstrate that robust deep visual models can be developed based on CaveSeg for fast semantic scene parsing of underwater cave environments. In particular, we formulate a novel transformer-based model that is computationally light and offers near real-time execution in addition to achieving state-of-the-art performance. Finally, we explore the design choices and implications of semantic segmentation for visual servoing by AUVs inside underwater caves. The proposed model and benchmark dataset open up promising opportunities for future research in autonomous underwater cave exploration and mapping.Comment: submitted for review in ICRA 2024. 10 pages, 9 figure

Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves

Underwater caves are challenging environments that are crucial for water resource management, and for our understanding of hydro-geology and history. Mapping underwater caves is a time-consuming, labor-intensive, and hazardous operation. For autonomous cave mapping by underwater robots, the major challenge lies in vision-based estimation in the complete absence of ambient light, which results in constantly moving shadows due to the motion of the camera-light setup. Thus, detecting and following the caveline as navigation guidance is paramount for robots in autonomous cave mapping missions. In this paper, we present a computationally light caveline detection model based on a novel Vision Transformer (ViT)-based learning pipeline. We address the problem of scarce annotated training data by a weakly supervised formulation where the learning is reinforced through a series of noisy predictions from intermediate sub-optimal models. We validate the utility and effectiveness of such weak supervision for caveline detection and tracking in three different cave locations: USA, Mexico, and Spain. Experimental results demonstrate that our proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring good generalization performance while offering 10+ FPS on single-board (Jetson TX2) devices

Morphological study of Red lake in Dinaric karst based on terrestrial laser scaning and sonar system

Red Lake (Dinaric karst, Croatia) is an exceptional karst phenomenon, worldwide known for its beauty and extreme depth. Even so, through the history of Red Lake’s research there were many controversies in the conclusions and the theories concerning its genesis, geomorphology and hydrology. The aim of this work is to give an overview of existing findings about Red Lake as well as to present the newest research results gained with the help of emerging technologies based on terrestrial laser scanning and hydro acoustics. The measuring was conducted during September 2013. A new generation of equipment developed to advance the geoscientific research was deployed during the field work. The gathered data enabled a thorough analysis which led to new important findings on Red Lake. Deployment of Remotely operated underwater vehicle (ROV) equipped with imaging sonar resulted in the first hydro acoustic survey of the lake. Some of the results confirmed the already known and well documented characteristics of Red Lake whereas others disputed the widely accepted assumptions in the scientific community and the general public. The presented research generated, for the first time, a DEM for Red Lake which allowed a good estimate on the lake’s volume. For the recorded maximum water level at 311 m a.s.l. the volume of water stored is 8.24 x 106 m3. A temperature profile of the lake was recorded during the field work and it offered an insight on possible water mixing through karst conduits near the surface of the water. The application of TLS and ROV sonar systems considerably improved the understanding of Red Lake’s morphology