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

2019 ◽  
Vol 9 (1) ◽  
pp. 5 ◽  
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.

SIMULTANEOUS COOPERATIVE LOCALIZATION FOR AUVs USING RANGE-ONLY SENSORS

2011 ◽  
Vol 08 (02) ◽  
pp. 117-132 ◽  
Author(s):  
ALI JABAR RASHIDI ◽  
SAEED MOHAMMADLOO
Keyword(s):  
Low Cost ◽  
Autonomous Underwater Vehicles ◽  
Measurement Unit ◽  
Cooperative Localization ◽  
Position Information ◽  
Localization Accuracy ◽  
Long Baseline ◽  
Localization And Mapping ◽  
Filter Process ◽  
Sonar Sensor

The absence of GPS underwater makes navigation for autonomous underwater vehicles (AUVs) a challenge. Moreover, the use of static beacons in the form of a long baseline (LBL) array limits the operation area to a few square kilometers and requires substantial deployment effort before operations. In this paper, an algorithm for cooperative localization of AUVs is proposed. We describe a form of cooperative Simultaneous Localization and Mapping (SLAM). Each of the robots in the group is equipped with an Inertial Measurement Unit (IMU) and some of them have a range-only sonar sensor that can determine the relative distance to the others. Two estimators, in the form of a Kalman filter, process the available position information from all the members of the team and produce a pose estimate for every one of them. Simulation results are presented for a typical localization example of three AUVs formation in a large environment and indirect trajectory. The results show that our proposed method offers good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, which validates that it is an economical and practical localization approach.

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.

scholarly journals A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zhan Wang ◽  
Alain Lambert
Keyword(s):  
Constraint Satisfaction Problem ◽  
Low Cost ◽  
Dead Reckoning ◽  
Constraint Propagation ◽  
Vehicle Localization ◽  
Practical Applications ◽  
Localization And Mapping ◽  
Position And Orientation ◽  
Map Data ◽  
Interval Constraint

Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper proposes an interval analysis based method to estimate the vehicle pose (position and orientation) in a consistent way, by fusing low-cost sensors and map data. We cast the localization problem into an Interval Constraint Satisfaction Problem (ICSP), solved via Interval Constraint Propagation (ICP) techniques. An interval map is built when a vehicle embedding expensive sensors navigates around the environment. Then vehicles with low-cost sensors (dead reckoning and monocular camera) can use this map for ego-localization. Experimental results show the soundness of the proposed method in achieving consistent localization.

scholarly journals A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090320
Author(s):  
Peng Li ◽  
Cai-yun Yang ◽  
Rui Wang ◽  
Shuo Wang
Keyword(s):  
Path Planning ◽  
Information Entropy ◽  
High Efficiency ◽  
Dead Reckoning ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Coverage Area ◽  
High Coverage ◽  
Full Coverage ◽  
Localization And Mapping

The efficiency of exploration in an unknown scene and full coverage of the scene are essential for a robot to complete simultaneous localization and mapping actively. However, it is challenging for a robot to explore an unknown environment with high efficiency and full coverage autonomously. In this article, we propose a novel exploration path planning method based on information entropy. An information entropy map is first constructed, and its boundary features are extracted. Then a Dijkstra-based algorithm is applied to generate candidate exploration paths based on the boundary features. The dead-reckoning algorithm is used to predict the uncertainty of the robot’s pose along each candidate path. The exploration path is selected based on exploration efficiency and/or high coverage. Simulations and experiments are conducted to evaluate the proposed method’s effectiveness. The results demonstrated that the proposed method achieved not only higher exploration efficiency but also a larger coverage area.

scholarly journals A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 802 ◽  
Author(s):  
Elena López ◽  
Sergio García ◽  
Rafael Barea ◽  
Luis Bergasa ◽  
Eduardo Molinos ◽  
...  
Keyword(s):  
Low Cost ◽  
Simultaneous Localization And Mapping ◽  
Micro Aerial Vehicles ◽  
Aerial Vehicles ◽  
Localization And Mapping

scholarly journals Integration of Low-Cost GNSS and Monocular Cameras for Simultaneous Localization and Mapping

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2193 ◽  
Author(s):  
Xiao Chen ◽  
Weidong Hu ◽  
Lefeng Zhang ◽  
Zhiguang Shi ◽  
Maisi Li
Keyword(s):  
Low Cost ◽  
Simultaneous Localization And Mapping ◽  
Localization And Mapping

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

Robotics ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 45 ◽  
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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