Sonar-based simultaneous localization and mapping for autonomous underwater vehicles

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

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Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization

Applied Sciences ◽

10.3390/app9071428 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1428 ◽

Cited By ~ 4

Author(s):

Ran Wang ◽

Xin Wang ◽

MingMing Zhu ◽

YinFu Lin

Keyword(s):

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Visual Localization ◽

Visualization Method ◽

Localization And Mapping ◽

Real Time Visualization ◽

Novel Method ◽

Vision Based Localization ◽

Slam Algorithm ◽

Localization Systems

Autonomous underwater vehicles (AUVs) are widely used, but it is a tough challenge to guarantee the underwater location accuracy of AUVs. In this paper, a novel method is proposed to improve the accuracy of vision-based localization systems in feature-poor underwater environments. The traditional stereo visual simultaneous localization and mapping (SLAM) algorithm, which relies on the detection of tracking features, is used to estimate the position of the camera and establish a map of the environment. However, it is hard to find enough reliable point features in underwater environments and thus the performance of the algorithm is reduced. A stereo point and line SLAM (PL-SLAM) algorithm for localization, which utilizes point and line information simultaneously, was investigated in this study to resolve the problem. Experiments with an AR-marker (Augmented Reality-marker) were carried out to validate the accuracy and effect of the investigated algorithm.

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Active SLAM for Autonomous Underwater Exploration

Remote Sensing ◽

10.3390/rs11232827 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2827 ◽

Cited By ~ 3

Author(s):

Narcís Palomeras ◽

Marc Carreras ◽

Juan Andrade-Cetto

Keyword(s):

Autonomous Underwater Vehicles ◽

A Priori ◽

Underwater Vehicles ◽

View Planning ◽

Localization And Mapping ◽

Entropy Reduction ◽

Graph Slam ◽

Slam Algorithm ◽

Iterative Closest Point Algorithm ◽

Active Localization

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.

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NLMAP - visual-based self localization and mapping for Autonomous Underwater Vehicles

OCEANS 2008 ◽

10.1109/oceans.2008.5152106 ◽

2008 ◽

Author(s):

Silvia Botelho ◽

Paulo Drews ◽

Gabriel Leivas

Keyword(s):

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Localization And Mapping

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Terrain-Based Localization and Mapping for Autonomous Underwater Vehicles using Particle Filters with Marine Gravity Anomalies

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2018.09.498 ◽

2018 ◽

Vol 51 (29) ◽

pp. 354-359 ◽

Cited By ~ 2

Author(s):

Parth Pasnani ◽

Mae L. Seto

Keyword(s):

Particle Filters ◽

Autonomous Underwater Vehicles ◽

Gravity Anomalies ◽

Underwater Vehicles ◽

Localization And Mapping ◽

Marine Gravity

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Proving the existence of loops in robot trajectories

The International Journal of Robotics Research ◽

10.1177/0278364918808367 ◽

2018 ◽

Vol 37 (12) ◽

pp. 1500-1516 ◽

Cited By ~ 1

Author(s):

Simon Rohou ◽

Peter Franek ◽

Clément Aubry ◽

Luc Jaulin

Keyword(s):

Topological Degree ◽

Reliable Method ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Differential Topology ◽

Optimal Method ◽

Verification Tool ◽

Localization And Mapping ◽

Key Points ◽

Bounded Error

In this paper we present a reliable method to verify the existence of loops along the uncertain trajectory of a robot, based on proprioceptive measurements only, within a bounded-error context. The loop closure detection is one of the key points in simultaneous localization and mapping (SLAM) methods, especially in homogeneous environments with difficult scenes recognitions. The proposed approach is generic and could be coupled with conventional SLAM algorithms to reliably reduce their computing burden, thus improving the localization and mapping processes in the most challenging environments such as unexplored underwater extents. To prove that a robot performed a loop whatever the uncertainties in its evolution, we employ the notion of topological degree that originates in the field of differential topology. We show that a verification tool based on the topological degree is an optimal method for proving robot loops. This is demonstrated both on datasets from real missions involving autonomous underwater vehicles and by a mathematical discussion.

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A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

Robotics ◽

10.3390/robotics7030045 ◽

2018 ◽

Vol 7 (3) ◽

pp. 45 ◽

Cited By ~ 13

Author(s):

Chang Chen ◽

Hua Zhu ◽

Menggang Li ◽

Shaoze You

Keyword(s):

Mobile Robotics ◽

Autonomous Underwater Vehicles ◽

Simultaneous Localization And Mapping ◽

Research Directions ◽

Localization Accuracy ◽

Core Theory ◽

Localization And Mapping ◽

Comprehensive Survey ◽

Self Driving Cars ◽

Future Work

Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.

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A Novel Underwater Simultaneous Localization and Mapping Online Algorithm Based on Neural Network

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9010005 ◽

2019 ◽

Vol 9 (1) ◽

pp. 5 ◽

Cited By ~ 1

Author(s):

Guangchao Hou ◽

Qi Shao ◽

Bo Zou ◽

Liwen Dai ◽

Zhe Zhang ◽

...

Keyword(s):

Online Algorithm ◽

Low Cost ◽

Autonomous Underwater Vehicles ◽

Low Frequency ◽

Dead Reckoning ◽

Simultaneous Localization And Mapping ◽

Low Complexity ◽

Petroleum Engineering ◽

Long Baseline ◽

Localization And Mapping

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

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