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

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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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-LiDAR Fusion based Simultaneous Localization and Mapping

Sensors ◽

10.3390/s20072068 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2068 ◽

Cited By ~ 7

Author(s):

César Debeunne ◽

Damien Vivet

Keyword(s):

Autonomous Navigation ◽

Mobile Robotics ◽

State Of The Art ◽

Autonomous Underwater Vehicles ◽

Simultaneous Localization And Mapping ◽

Underwater Vehicles ◽

Visual Features ◽

Localization And Mapping ◽

Comprehensive Survey ◽

Self Driving Cars

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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