scholarly journals The Simultaneous Localization and Mapping (SLAM)-An Overview

2021 ◽  
Vol 1 (02) ◽  
pp. 34-45
Author(s):  
Samer Karam ◽  
Bashar Alsadik
Keyword(s):  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Computer Hardware ◽  
Problem Solution ◽  
Active Sensors ◽  
Practical Applications ◽  
Radio Detection ◽  
Localization And Mapping ◽  
Mathematical Techniques ◽  
Pose Graph Optimization

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.

scholarly journals Autonomous navigation in unknown environment using sliding mode SLAM and genetic algorithm

2021 ◽  
Author(s):  
Salvador Ortiz ◽  
Wen Yu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Sliding Mode Control ◽  
Autonomous Navigation ◽  
Sliding Mode ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Bounded Uncertainties

In this paper, sliding mode control is combined with the classical simultaneous localization and mapping (SLAM) method. This combination can overcome the problem of bounded uncertainties in SLAM. With the help of genetic algorithm, our novel path planning method shows many advantages compared with other popular methods.

scholarly journals Robot Simultaneous Localization and Mapping Based on Self-Detected Waypoint

2016 ◽  
Vol 16 (2) ◽  
pp. 212-221
Author(s):  
Yingmin Yi ◽  
Xiangru Hu
Keyword(s):  
Autonomous Navigation ◽  
Simulation Experiment ◽  
Simultaneous Localization And Mapping ◽  
Central Point ◽  
Point Of Interest ◽  
Area Of Interest ◽  
Localization And Mapping

Abstract The point of interest in this paper is the main content of autonomous navigation of robots. An algorithm for robot Simultaneous Localization And Mapping (SLAM) based on self-detected waypoint is introduced to realize robot’s mapping in its area of interest. Robot’s next step waypoint is performed using characteristics of large information in the area of interest and dense landmark, clustering the landmark in the area of interest, and guiding robot’s movement with clustered central point. Robot clusters the observed area in its observation every time. It takes the clustered center based on the largest number of landmarks as the waypoint of the next step. Simulation experiment shows, that due to robot’s movement toward the area of dense landmarks, the proposed method increases the number of landmarks observed by the robot and frequency of observation is increased. The proposed method enhances accuracy of robot’s positioning and the robot realizes to detect its waypoint autonomously.

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 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.

A Review: Simultaneous Localization and Mapping Algorithms

2015 ◽  
Vol 73 (2) ◽  
Author(s):  
Saif Eddine Hadji ◽  
Suhail Kazi ◽  
Tang Howe Hing ◽  
Mohamed Sultan Mohamed Ali
Keyword(s):  
Autonomous Navigation ◽  
State Of The Art ◽  
Simultaneous Localization And Mapping ◽  
The State ◽  
Sensor Data ◽  
Current Position ◽  
Mapping Algorithms ◽  
The Past ◽  
Localization And Mapping ◽  
Environmental Map

Simultaneous Localization and Mapping (SLAM) involves creating an environmental map based on sensor data, while concurrently keeping track of the robot’s current position. Efficient and accurate SLAM is crucial for any mobile robot to perform robust navigation. It is also the keystone for higher-level tasks such as path planning and autonomous navigation. The past two decades have seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. In this paper, we will review the two common families of SLAM algorithms: Kalman filter with its variations and particle filters. This article complements other surveys in this field by reviewing the representative algorithms and the state-of-the-art in each family. It clearly identifies the inherent relationship between the state estimation via the KF versus PF techniques, all of which are derivations of Bayes rule.

Indoor Autonomous Navigation Technology Research Based on Lidar for Quad-Copter Aerial Robot

2014 ◽  
Vol 538 ◽  
pp. 371-374
Author(s):  
Zhi Jun Bai ◽  
Yang Feng Ji ◽  
Liao Ni Wu ◽  
Qi Lin
Keyword(s):  
Navigation System ◽  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Technology Research ◽  
Unknown Environment ◽  
Monocular Camera ◽  
Localization And Mapping ◽  
Aerial Robot ◽  
Test Result ◽  
Autonomous Navigation System

An indoor autonomous navigation system without GPS has been developed, based on the platform of quad-copter, which may fulfill the task of searching, identifying and entering the target room in a building with multi-rooms corridor. A pose sensor was utilized to stabilize the aircraft. The SLAM (Simultaneous Localization and Mapping) and plan route in unknown environment have been created by a 2D lidar. A calibrated monocular camera has been used to recognize different marks to make sure the vehicle to enter the target room. The test result showed that the indoor autonomous navigation technology based on lidar for quad-copter aerial robot is feasible and successful.

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 Mapping and Navigation for Indoor Robots under ROS: An Experimental Analysis

2019 ◽  
Author(s):  
Bruno M. F. da Silva ◽  
Rodrigo S. Xavier ◽  
Luiz M. G. Gonçalves
Keyword(s):  
Operating System ◽  
Experimental Analysis ◽  
Autonomous Navigation ◽  
Robot Navigation ◽  
Simultaneous Localization And Mapping ◽  
Indoor Environments ◽  
Robot Operating System ◽  
Localization And Mapping ◽  
Application Requirements

Since it was proposed, the Robot Operating System (ROS) has fostered solutions for various problems in robotics in the form of ROS packages. One of these problems is Simultaneous Localization and Mapping (SLAM), a problem solved by computing the robot pose and a map of its environment of operation at the same time. The increasingly availability of robot kits ready to be programmed and also of RGB-D sensors often pose the question of which SLAM package should be used given the application requirements. When the SLAM subsystem must deliver estimates for robot navigation, as is the case of applications involving autonomous navigation, this question is even more relevant. This work introduces an experimental analysis of GMapping and RTAB-Map, two ROS compatible SLAM packages, regarding their SLAM accuracy, quality of produced maps and use of produced maps in navigation tasks. Our analysis aims ground robots equipped with RGB-D sensors for indoor environments and is supported by experiments conducted on datasets from simulation, benchmarks and from our own robot.

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