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.

scholarly journals Distributed and collaborative monocular simultaneous localization and mapping for multi-robot systems in large-scale environments

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878017 ◽  
Author(s):  
Hui Zhang ◽  
Xieyuanli Chen ◽  
Huimin Lu ◽  
Junhao Xiao
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Simultaneous Localization And Mapping ◽  
Small Scale ◽  
Data Set ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Mapping System ◽  
Robot Systems ◽  
Multi Robot

In this article, we propose a distributed and collaborative monocular simultaneous localization and mapping system for the multi-robot system in large-scale environments, where monocular vision is the only exteroceptive sensor. Each robot estimates its pose and reconstructs the environment simultaneously using the same monocular simultaneous localization and mapping algorithm. Meanwhile, they share the results of their incremental maps by streaming keyframes through the robot operating system messages and the wireless network. Subsequently, each robot in the group can obtain the global map with high efficiency. To build the collaborative simultaneous localization and mapping architecture, two novel approaches are proposed. One is a robust relocalization method based on active loop closure, and the other is a vision-based multi-robot relative pose estimating and map merging method. The former is used to solve the problem of tracking failures when robots carry out long-term monocular simultaneous localization and mapping in large-scale environments, while the latter uses the appearance-based place recognition method to determine multi-robot relative poses and build the large-scale global map by merging each robot’s local map. Both KITTI data set and our own data set acquired by a handheld camera are used to evaluate the proposed system. Experimental results show that the proposed distributed multi-robot collaborative monocular simultaneous localization and mapping system can be used in both indoor small-scale and outdoor large-scale environments.

scholarly journals A Review: Simultaneous Localization and Mapping in Application to Autonomous Robot

2018 ◽  
Author(s):  
Olusanya Agunbiade ◽  
Tranos Zuva
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Research Direction ◽  
Autonomous Robot ◽  
Unknown Environment ◽  
Computational Problem ◽  
Localization And Mapping ◽  
Open Issues

The important characteristic that could assist in autonomous navigation is the ability of a mobile robot to concurrently construct a map for an unknown environment and localize itself within the same environment. This computational problem is known as Simultaneous Localization and Mapping (SLAM). In literature, researchers have studied this approach extensively and have proposed a lot of improvement towards it. More so, we are experiencing a steady transition of this technology to industries. However, there are still setbacks limiting the full acceptance of this technology even though the research had been conducted over the last 30 years. Thus, to determine the problems facing SLAM, this paper conducted a review on various foundation and recent SLAM algorithms. Challenges and open issues alongside the research direction for this area were discussed. However, towards addressing the problem discussed, a novel SLAM technique will be proposed.

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 Mobile Robot Simultaneous Localization and Mapping Based on a Monocular Camera

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Songmin Jia ◽  
Ke Wang ◽  
Xiuzhi Li
Keyword(s):  
Mobile Robot ◽  
Pose Estimation ◽  
Estimation Method ◽  
Simultaneous Localization And Mapping ◽  
Map Building ◽  
Outlier Rejection ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Great Performance ◽  
Feature Based

This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) algorithm for mobile robot. In this proposed method, the tracking and mapping procedures are split into two separate tasks and performed in parallel threads. In the tracking thread, a ground feature-based pose estimation method is employed to initialize the algorithm for the constraint moving of the mobile robot. And an initial map is built by triangulating the matched features for further tracking procedure. In the mapping thread, an epipolar searching procedure is utilized for finding the matching features. A homography-based outlier rejection method is adopted for rejecting the mismatched features. The indoor experimental results demonstrate that the proposed algorithm has a great performance on map building and verify the feasibility and effectiveness of the proposed algorithm.

A Study of Autonomous Navigation of a Robot Model based on SLAM, ROS, and Kinect

2018 ◽  
Vol 7 (3.33) ◽  
pp. 28
Author(s):  
Asilbek Ganiev ◽  
Kang Hee Lee
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Target Point ◽  
Robot Operating System ◽  
Model Based ◽  
Localization And Mapping ◽  
Starting Point

In this paper, we used a robot operating system (ROS) that is designed to work with mobile robots. ROS provides us with simultaneous localization and mapping of the environment, and here it is used to autonomously navigate a mobile robot simulator between specified points. Also, when the mobile robot automatically navigates between the starting point and the target point, it bypasses obstacles; and if necessary, sets a new path of the route to reach the goal point.  

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