scholarly journals Active SLAM for Autonomous Underwater Exploration

2019 ◽  
Vol 11 (23) ◽  
pp. 2827 ◽  
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.

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

2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
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.

scholarly journals A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles†

2012 ◽  
Vol 46 (2) ◽  
pp. 32-44 ◽  
Author(s):  
Laura Sorbi ◽  
Graziano Pio De Capua ◽  
Jean-Guy Fontaine ◽  
Laura Toni
Keyword(s):  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Underwater Vehicles ◽  
Fixed Target ◽  
Marine Research ◽  
Detection And Tracking ◽  
Mobile Target ◽  
Overall Performance ◽  
Behavior Based ◽  
Unknown Area

AbstractDue to its applications in marine research, oceanographic, and undersea exploration, autonomous underwater vehicles (AUVs) and the related control algorithms recently have been under intense investigation. In this work, we address target detection and tracking issues, proposing a control strategy that is able to benefit from the cooperation among robots within the fleet. In particular, we introduce a behavior-based planner for cooperative AUVs, proposing an algorithm that is able to search and recognize targets in both static and dynamic scenarios. With no a priori information about the surrounding environment, robots cover an unknown area with the goal of finding objects of interest. When a target is found, the AUVs’ goal is to classify (fixed target) or track (mobile target) the target, with no information about target trajectory and with formation constraints. Results demonstrate the good overall performance of the proposed algorithm in both scenarios.

scholarly journals REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 329-335
Author(s):  
N. Botteghi ◽  
B. Sirmacek ◽  
R. Schulte ◽  
M. Poel ◽  
C. Brune
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
A Priori ◽  
Simultaneous Localization And Mapping ◽  
Indoor Environments ◽  
Robot Localization ◽  
Robust Solution ◽  
Localization And Mapping ◽  
Reward Functions ◽  
Slam Algorithm

Abstract. In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.

scholarly journals Proving the existence of loops in robot trajectories

2018 ◽  
Vol 37 (12) ◽  
pp. 1500-1516 ◽  
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.

A NOVEL ADAPTIVE NEURO-FUZZY UNSCENTED KALMAN FILTER FOR SLAM

2011 ◽  
Vol 08 (01) ◽  
pp. 223-243 ◽  
Author(s):  
RAMAZAN HAVANGI ◽  
MOHAMMAD TESHNEHLAB ◽  
MOHAMMAD ALI NEKOUI
Keyword(s):  
Kalman Filter ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
A Priori ◽  
Inference System ◽  
Localization And Mapping ◽  
Neuro Fuzzy ◽  
Measurement Noise Covariance ◽  
Slam Algorithm ◽  
Sampling Instant

Extended Kalman filter (EKF) has been used as a popular choice to solve simultaneous localization and mapping (SLAM) problem. However, SLAM algorithm based on EKF-SLAM has two serious drawbacks, namely the linear approximation of nonlinear functions and the calculation of Jacobin matrices. For solving these problems, SLAM algorithm based on unscented Kalman filter (UKF-SLAM) has been recently proposed. However, the performance of the UKF-SLAM and thus the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices respectively denoted by Qk and Rk. Imprecise knowledge of these statistics can cause significant degradation in performance. This article proposes the development of an adaptive neuro-fuzzy UKF (ANFUKF) for SLAM. The Adaptive neuro-fuzzy attempts to estimate the elements of Rk matrix in the UKF-SLAM algorithm at each sampling instant when measurement updating step is carried out. The adaptive neuro-fuzzy inference system (ANFIS) supervises the performance of the UKF-SLAM with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (GD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much as possible. The simulation results show the effectiveness of the proposed algorithm.

scholarly journals A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2068 ◽  
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.

scholarly journals Test Results of the Long Baseline Navigation Solutions under a Large a Priori Position Uncertainty

2022 ◽  
Vol 1215 (1) ◽  
pp. 012006
Author(s):  
V.V. Bogomolov
Keyword(s):  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Underwater Vehicles ◽  
Posteriori Probability ◽  
Global Maximum ◽  
Test Results ◽  
Final Solution ◽  
Position Uncertainty ◽  
Modified Method ◽  
Long Baseline

Abstract A method is proposed for long baseline navigation of autonomous underwater vehicles (AUV) to be used in the case of a large a priori position uncertainty. The new modified method is based on the iterated Kalman filter (IKF) working with different initial linearization points. The final solution is calculated by clustering and weighting the IKF results. This approach allows position estimates to be determined in accordance with the global maximum of posteriori probability density of coordinates. The test results obtained with the use of three beacons and an underwater vehicle are presented.

scholarly journals Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models

2021 ◽  
Vol 9 (11) ◽  
pp. 1183
Author(s):  
Matteo Bresciani ◽  
Francesco Ruscio ◽  
Simone Tani ◽  
Giovanni Peralta ◽  
Andrea Timperi ◽  
...  
Keyword(s):  
Path Planning ◽  
Information Content ◽  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Posidonia Oceanica ◽  
Underwater Vehicles ◽  
Information Gathering ◽  
Technological Developments ◽  
Path Planner ◽  
Areas Of Interest

Recent technological developments have paved the way to the employment of Autonomous Underwater Vehicles (AUVs) for monitoring and exploration activities of marine environments. Traditionally, in information gathering scenarios for monitoring purposes, AUVs follow predefined paths that are not efficient in terms of information content and energy consumption. Informative Path Planning (IPP) represents a valid alternative, defining the path that maximises the gathered information. This work proposes a Genetic Path Planner (GPP), which consists in an IPP strategy based on a Genetic Algorithm, with the aim of generating a path that simultaneously maximises the information gathered and the coverage of the inspected area. The proposed approach has been tested offline for monitoring and inspection applications of Posidonia Oceanica (PO) in three different geographical areas. The a priori knowledge about the presence of PO, in probabilistic terms, has been modelled utilising a Gaussian Process (GP), trained on real marine data. The GP estimate has then been exploited to retrieve an information content of each position in the areas of interest. A comparison with other two IPP approaches has been carried out to assess the performance of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document