A Particle Filter SLAM Approach to Online Iceberg Drift Estimation From an AUV

2017 ◽  
Author(s):  
Petter Norgren ◽  
Roger Skjetne
Keyword(s):  
Particle Filter ◽  
Simulation Study ◽  
Autonomous Underwater Vehicles ◽  
Automated Mapping ◽  
Sensor Model ◽  
Drift Estimation ◽  
Localization And Mapping ◽  
Iceberg Drift ◽  
Information Filters ◽  
Drift Trajectory

Using autonomous underwater vehicles (AUVs) for mapping the underwater topography of sea-ice and icebergs, or detecting keels of ice ridges, is foreseen as an enabling technology in future Arctic offshore operations. This paper presents a method for online iceberg drift estimation using a Simultaneous Localization and Mapping (SLAM) approach using an AUV with a multi-beam echosounder (MBE) during such survey/monitoring operations. Iceberg drift is affected by wind, current, and Coriolis forces. This can be hard to predict, making automated mapping of icebergs difficult. The method proposed in this paper estimates the iceberg’s pose using a particle filter, where each particle uses extended information filters to estimate the topography of the iceberg. A grid map is used to store the iceberg topography, and distributed particle mapping is used to avoid expensive copy operations during particle resampling. The proposed method is verified through a simulation study, using a 6 DOF AUV model, an MBE sensor model, and an iceberg topography taken from the PERD iceberg sightings database. The method is able to provide a georeferenced iceberg position, thus, estimating the iceberg’s drift trajectory. A topography estimate of the iceberg, corrected for iceberg drift, is also generated. Furthermore, the algorithm estimates the iceberg drift velocity, as well as the relative iceberg-AUV pose, for use in future iceberg mapping guidance algorithms. The simulation study illustrates the performance of the method, and a short execution time analysis is presented to illustrate the method’s real-time potential.

scholarly journals An Improved Localization Method for the Transition between Autonomous Underwater Vehicle Homing and Docking

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2468
Author(s):  
Ri Lin ◽  
Feng Zhang ◽  
Dejun Li ◽  
Mingwei Lin ◽  
Gengli Zhou ◽  
...  
Keyword(s):  
Data Exchange ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Optimization Method ◽  
Short Baseline ◽  
Localization Accuracy ◽  
Localization Method ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Update Rate

Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.

scholarly journals Study on the Arctic Underwater Terrain-Aided Navigation Based on Fuzzy-Particle Filter

2021 ◽  
Author(s):  
Yanji Liu ◽  
Guichen Zhang ◽  
Zhijian Huang
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Arctic Region ◽  
The Arctic ◽  
Bilinear Interpolation ◽  
Low Resolution ◽  
Continuous Map ◽  
Map Resolution ◽  
Navigation Accuracy ◽  
The Arctic Region

AbstractThe ultra-low resolution underwater terrain maps of the Arctic region reduce the localization and navigation accuracy of the underwater vehicle relying on terrain-aided navigation. In this paper, we study the navigation ability of Autonomous Underwater Vehicles (AUVs) under the ultralow-resolution terrain map. Firstly, the low-resolution map is transformed into a continuous map by bilinear interpolation. Then, a Terrain-Aided Navigation (TAN) system based on the Particle Filter (PF) is constructed to estimate the state of AUV position by particles. Particles of a random distribution of fixed variance can effectively track targets. However, a fixed variance distribution is not well adapted to many different situations. To improve navigation accuracy and robustness, fuzzy logic is used to estimate the distribution variance of particles under the current terrain gradient dynamically. The simulation results show that our proposed Fuzzy-PF TAN system is robust under various current disturbance situations. The position error of our system is within a map resolution unit of 500 m.

TSLAM: Tethered simultaneous localization and mapping for mobile robots

2017 ◽  
Vol 36 (12) ◽  
pp. 1363-1386 ◽  
Author(s):  
Patrick McGarey ◽  
Kirk MacTavish ◽  
François Pomerleau ◽  
Timothy D Barfoot
Keyword(s):  
Mobile Robots ◽  
Particle Filter ◽  
Low Cost ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Anchor Point ◽  
Outlier Rejection ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Anchor Points

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

Implementation of FastSLAM2.0 on an Embedded System and HIL Validation using Different Sensors Data

2015 ◽  
Vol 6 (2) ◽  
pp. 88-116
Author(s):  
Abouzahir Mohamed ◽  
Elouardi Abdelhafid ◽  
Bouaziz Samir ◽  
Latif Rachid ◽  
Tajer Abdelouahed
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Particle Filter ◽  
Real Time ◽  
Autonomous Navigation ◽  
Hardware In The Loop ◽  
Heterogeneous Architectures ◽  
Localization And Mapping ◽  
Embedded Applications ◽  
Robotic Applications

The improved particle filter based simultaneous localization and mapping (SLAM) has been developed for many robotic applications. The main purpose of this article is to demonstrate that recent heterogeneous architectures can be used to implement the FastSLAM2.0 and can greatly help to design embedded systems based robot applications and autonomous navigation. The algorithm is studied, optimized and evaluated with a real dataset using different sensors data and a hardware in the loop (HIL) method. Authors have implemented the algorithm on a system based embedded applications. Results demonstrate that an optimized FastSLAM2.0 algorithm provides a consistent localization according to a reference. Such systems are suitable for real time SLAM applications.

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

2020 ◽  
Vol 42 (11) ◽  
pp. 1946-1959
Author(s):  
Jiayu He ◽  
Ye Li ◽  
Jian Cao ◽  
Yueming Li ◽  
Yanqing Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Gaussian Kernel ◽  
Filter Method ◽  
Grey Prediction ◽  
Sea Trial ◽  
False Diagnosis

The overall architectural complexity of autonomous underwater vehicles continuous to increase, enlarging the probability of fault occurrence in subsystems. Estimating the thrust loss by particle filter provided a useful method to detect the fault in propeller subsystem. In order to detect the fault in propellers as early as possible, the particle filter direct prediction method could amplify the fault trend and detect the fault earlier, but at the same time increase the possibility of false diagnosis. Therefore, a more accurate fault diagnosis method was required to discover the fault early and decrease the occurrence of false diagnosis. In this paper, an improved particle filter prediction method was proposed, combining the advantage of grey prediction to forecast the motion state, reducing the uncertainty in particle filter direct prediction process. Besides, the Gaussian kernel function was applied to judge the credibility of the prediction result, decreasing the possibility of the false diagnosis. In the experiments with simulated working conditions data and a section of actual sea trial data with propeller fault, the proposed method detected the fault earlier compared with the original particle filter method, and reduced the false diagnosis rate compared with the particle filter direct prediction method. The results show that the proposed method is effective in detecting the fault early with low false diagnosis.

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 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 PROPELLER FAULT DIAGNOSIS BASED ON A RANK PARTICLE FILTER FOR AUTONOMOUS UNDERWATER VEHICLES

Brodogradnja ◽  
2018 ◽  
Vol 69 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Jiayu He ◽  
◽  
Ye Li ◽  
Yanqing Jiang ◽  
Yueming Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles
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