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.

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 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.

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.

scholarly journals A Brief Survey on SLAM Methods in Autonomous Vehicle

2018 ◽  
Vol 7 (4.27) ◽  
pp. 38 ◽  
Author(s):  
Talha Takleh Omar Takleh ◽  
Nordin Abu Bakar ◽  
Shuzlina Abdul Rahman ◽  
Raseeda Hamzah ◽  
Zalilah Abd Aziz
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Simultaneous Localization And Mapping ◽  
General Concept ◽  
Research Community ◽  
Current Trends ◽  
Localization And Mapping ◽  
Pros And Cons ◽  
Self Driving Cars

The overall purpose of this paper is to provide an introductory survey in the area of Simultaneous Localization and Mapping (SLAM) particularly its utilization in autonomous vehicle or more specifically in self-driving cars, especially after the release of commercial semi-autonomous car like the Tesla vehicles as well as the Google Waymo vehicle. Before we begin diving into the concept of SLAM, we need to understand the importance of SLAM and problems that expand to the various methods developed by numerous researchers to solve it. Thus, in this paper we will start by giving the general concept behind SLAM, followed by sharing details of its different categories and the various methods that form the SLAM function in today’s autonomous vehicles; which can solve the SLAM problem. These methods are the current trends that are widely focused in the research community in producing solutions to the SLAM problem; not only in autonomous vehicle but in the robotics field as well. Next, we will compare each of these methods in terms of its pros and cons before concluding the paper by looking at future SLAM challenges. 

scholarly journals Feature points selection with flocks of features constraint for visual simultaneous localization and mapping

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141666678
Author(s):  
Hong Liu ◽  
Zhi Wang ◽  
Pengjin Chen
Keyword(s):  
Mobile Robots ◽  
Simultaneous Localization And Mapping ◽  
Selection Method ◽  
Feature Point ◽  
Feature Points ◽  
Point Selection ◽  
Localization Accuracy ◽  
Surrounding Environment ◽  
Localization And Mapping ◽  
Crucial Problem

Simultaneous localization and mapping is a crucial problem for mobile robots, which estimates the surrounding environment (the map) and, at the same time, computes the robot location in it. Most researchers working on simultaneous localization and mapping focus on localization accuracy. In visual simultaneous localization and mapping , localization is to calculate the robot’s position relative to the landmarks, which corresponds to the feature points in images. Therefore, feature points are of importance to localization accuracy and should be selected carefully. This article proposes a feature point selection method to improve the localization accuracy. First, theoretical and numerical analyses are conducted to demonstrate the importance of distribution of feature points. Then, an algorithm using flocks of features is proposed to select feature points. Experimental results show that the proposed flocks of features selector implemented in visual simultaneous localization and mapping enhances the accuracy of both localization and mapping, verifying the necessity of feature point selection.

scholarly journals Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU

2021 ◽  
Vol 229 ◽  
pp. 01023
Author(s):  
Rachid Latif ◽  
Kaoutar Dahmane ◽  
Monir Amraoui ◽  
Amine Saddik ◽  
Abdelouahed Elouardi
Keyword(s):  
Execution Time ◽  
Autonomous Navigation ◽  
Heterogeneous System ◽  
Mobile Robotics ◽  
Simultaneous Localization And Mapping ◽  
Real Problem ◽  
System Type ◽  
Localization And Mapping ◽  
Slam Algorithm

Localization and mapping are a real problem in robotics which has led the robotics community to propose solutions for this problem... Among the competitive axes of mobile robotics there is the autonomous navigation based on simultaneous localization and mapping (SLAM) algorithms: in order to have the capacity to track the localization and the cartography of robots, that give the machines the power to move in an autonomous environment. In this work we propose an implementation of the bio-inspired SLAM algorithm RatSLAM based on a heterogeneous system type CPU-GPU. The evaluation of the algorithm showed that with C/C++ we have an executing time of 170.611 ms with a processing of 5 frames/s and for the implementation on a heterogeneous system we used CUDA as language with an execution time of 160.43 ms.

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.

scholarly journals Active Mapping and Robot Exploration: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2445
Author(s):  
Iker Lluvia ◽  
Elena Lazkano ◽  
Ander Ansuategi
Keyword(s):  
Prior Information ◽  
Mobile Robotics ◽  
Research Work ◽  
Simultaneous Localization And Mapping ◽  
Human Operator ◽  
Comprehensive Review ◽  
Minimum Error ◽  
Localization And Mapping

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.

scholarly journals Overview of Modern Approaches to Visual Odometry

2019 ◽  
pp. 5-14
Author(s):  
Mikhail A. Terekhov ◽  
Keyword(s):  
Scientific Community ◽  
Simultaneous Localization And Mapping ◽  
Visual Odometry ◽  
Bundle Adjustment ◽  
Research Directions ◽  
Localization And Mapping ◽  
Recent Method ◽  
Time Periods ◽  
Mapping Systems

In this paper we describe the tasks of Visual Odometry and Simultaneous Localization and Mapping systems along with their main applications. Next, we list some approaches used by the scientific community to create such systems in different time periods. We then proceed to explain in detail the more recent method based on bundle adjustment and show some of its variations for different applications. At last, we overview present-day research directions in the field of visual odometry and briefly present our work.

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