Application of Simultaneous Localization and Mapping for Large-scale Manipulators in Unknown Environments

2019 ◽  
Author(s):  
Petri Makinen ◽  
Mohammad M. Aref ◽  
Jouni Mattila ◽  
Sirpa Launis
Keyword(s):  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Unknown Environments ◽  
Localization And Mapping

Localization and Mapping for Indoor Navigation

2020 ◽  
pp. 930-954 ◽  
Author(s):  
Heba Gaber ◽  
Mohamed Marey ◽  
Safaa Amin ◽  
Mohamed F. Tolba
Keyword(s):  
System Architecture ◽  
Large Range ◽  
Simultaneous Localization And Mapping ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Challenging Problem ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Robotic Missions

Mapping and exploration for the purpose of navigation in unknown or partially unknown environments is a challenging problem, especially in indoor environments where GPS signals can't give the required accuracy. This chapter discusses the main aspects for designing a Simultaneous Localization and Mapping (SLAM) system architecture with the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. Achieving this capability makes these systems significantly more autonomous and ideal for a large range of applications, especially indoor navigation for humans and for robotic missions. This chapter surveys the existing algorithms and technologies used for localization and mapping and highlights on using SLAM algorithms for indoor navigation. Also the proposed approach for the current research is presented.

scholarly journals 3D Large-Scale Seabed Reconstruction for UUV Simultaneous Localization and Mapping

2008 ◽  
Vol 41 (1) ◽  
pp. 19-24
Author(s):  
Yvan R. Petillot ◽  
Joaquim Salvi ◽  
Elisabet Batlle
Keyword(s):  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Localization And Mapping

scholarly journals Collaborative Exploration based on Simultaneous Localization and Mapping

Robotics ◽  
2013 ◽  
pp. 1572-1599
Author(s):  
Domenec Puig
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Exploration Time ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Collaborative Exploration

This chapter focuses on the study of SLAM taking into account different strategies for modeling unknown environments, with the goal of comparing several methodologies and test them in real robots even if they are heterogeneous. The purpose is to combine them in order to reduce the exploration time. Indubitably, it is not an easy work because it is important to take into account the problem of integrating the information related with the changes into the map. In this way, it is necessary to obtain a representation of the surrounding in an efficiently way. Furthermore, the author is interested in the collaboration between robots, because it is well-known that a team of robots is capable of completing a given task faster than a single robot. This assumption will be checked by using both simulations and real robots in different experiments. In addition, the author combines the benefits of both vision-based and laser-based systems in the integration of the algorithms.

Localization and Mapping for Indoor Navigation

2017 ◽  
pp. 136-160 ◽  
Author(s):  
Heba Gaber ◽  
Mohamed Marey ◽  
Safaa Amin ◽  
Mohamed F. Tolba
Keyword(s):  
System Architecture ◽  
Large Range ◽  
Simultaneous Localization And Mapping ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Challenging Problem ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Robotic Missions

Mapping and exploration for the purpose of navigation in unknown or partially unknown environments is a challenging problem, especially in indoor environments where GPS signals can't give the required accuracy. This chapter discusses the main aspects for designing a Simultaneous Localization and Mapping (SLAM) system architecture with the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. Achieving this capability makes these systems significantly more autonomous and ideal for a large range of applications, especially indoor navigation for humans and for robotic missions. This chapter surveys the existing algorithms and technologies used for localization and mapping and highlights on using SLAM algorithms for indoor navigation. Also the proposed approach for the current research is presented.

Collaborative Exploration Based on Simultaneous Localization and Mapping

2013 ◽  
pp. 303-332
Author(s):  
Domenec Puig
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Exploration Time ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Collaborative Exploration

This chapter focuses on the study of SLAM taking into account different strategies for modeling unknown environments, with the goal of comparing several methodologies and test them in real robots even if they are heterogeneous. The purpose is to combine them in order to reduce the exploration time. Indubitably, it is not an easy work because it is important to take into account the problem of integrating the information related with the changes into the map. In this way, it is necessary to obtain a representation of the surrounding in an efficiently way. Furthermore, the author is interested in the collaboration between robots, because it is well-known that a team of robots is capable of completing a given task faster than a single robot. This assumption will be checked by using both simulations and real robots in different experiments. In addition, the author combines the benefits of both vision-based and laser-based systems in the integration of the algorithms.

scholarly journals An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4254 ◽  
Author(s):  
Le Jiang ◽  
Pengcheng Zhao ◽  
Wei Dong ◽  
Jiayuan Li ◽  
Mingyao Ai ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Large Scale ◽  
Field Experiments ◽  
Simultaneous Localization And Mapping ◽  
Detection Algorithm ◽  
Indoor Environments ◽  
Practical Applications ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Scanning Detection

Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use a laser-based SLAM (Simultaneous Localization and Mapping) algorithm to perform simultaneous localization and mapping to acquire the environment information around the robot. Then, according to the proposed algorithm, the 8 certain areas around the 8 directions which are developed from the robot’s center point are analyzed in order to calculate the probabilistic path vector of each area. Considering the requirements of efficient traverse and obstacle avoidance in practical applications, the proposal can find the optimal local path in a short time. In addition to local pathfinding, the global pathfinding is also introduced for unknown environments of large-scale and complex structures to reduce the repeated traverse. The field experiments in three typical indoor environments demonstrate that deviation of the planned path from the ideal path can be kept to a low level in terms of the path length and total time consumption. It is confirmed that the proposed algorithm is highly adaptable and practical in various indoor environments.

scholarly journals Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse

2020 ◽  
Vol 10 (2) ◽  
pp. 698 ◽  
Author(s):  
Feiren Wang ◽  
Enli Lü ◽  
Yu Wang ◽  
Guangjun Qiu ◽  
Huazhong Lu
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Indirect Method ◽  
Direct Method ◽  
Operating Time ◽  
Simultaneous Localization And Mapping ◽  
High Accuracy ◽  
Image Alignment ◽  
Localization And Mapping

The autonomous navigation of unmanned vehicles in GPS denied environments is an incredibly challenging task. Because cameras are low in price, obtain rich information, and passively sense the environment, vision based simultaneous localization and mapping (VSLAM) has great potential to solve this problem. In this paper, we propose a novel VSLAM framework based on a stereo camera. The proposed approach combines the direct and indirect method for the real-time localization of an autonomous forklift in a non-structured warehouse. Our proposed hybrid method uses photometric errors to perform image alignment for data association and pose estimation, extracts features from keyframes, and matches them to acquire the updated pose. By combining the efficiency of the direct method and the high accuracy of the indirect method, the approach achieves higher speed with comparable accuracy to a state-of-the-art method. Furthermore, the two step dynamic threshold feature extraction method significantly reduces the operating time. In addition, a motion model of the forklift is proposed to provide a more reasonable initial pose for direct image alignment based on photometric errors. The proposed algorithm is experimentally tested on a dataset constructed from a large scale warehouse with dynamic lighting and long corridors, and the results show that it can still successfully perform with high accuracy. Additionally, our method can operate in real time using limited computing resources.

scholarly journals Simulation and Experimental Evaluation of the EKF Simultaneous Localization and Mapping Algorithm on the Wifibot Mobile Robot

2018 ◽  
Vol 8 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Noura Ayadi ◽  
Nabil Derbel ◽  
Nicolas Morette ◽  
Cyril Novales ◽  
Gérard Poisson
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Research Field ◽  
Small Scale ◽  
Mapping Algorithm ◽  
Highly Active ◽  
Localization And Mapping ◽  
Active Research

Abstract In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.

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