Design of Mobile Robot Based on Cartographer SLAM Algorithm

Qing, Gao; Jia, Hao; Liu, Ya; Tian, Xincheng

doi:10.12783/dtcse/ica2019/30762

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…The interdependence of location and mapping increases the complexity of the problem and requires these two problems to be solved simultaneously [5]. Simultaneous Localization and Mapping (SLAM) is a prediction process in which the autonomous mobile robot can build a consistent map step by step while at the same time using that map to determine its location [2,8,9,10]. Algorithms such as Gmapping, Hector SLAM, and Karto SLAM have been developed to solve the SLAM problem in robotic applications [6].…”

Section: Introductionmentioning

confidence: 99%

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Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo

Aydemir¹,

Tekerek

GÖK

2021

El-Cezeri Fen Ve Mühendislik Dergisi

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An autonomous mobile robot needs a map of the environment and location information relative to the map. Simultaneous Localization and Mapping (SLAM) is a prediction process in which the autonomous mobile robot can use this map to determine its position while building a consistent map. The purpose of this study is to examine the effect of geometric objects on SLAM performance. In this direction, three different experimental areas including equilateral triangular prisms, square prisms and cylinders are designed in Gazebo. The fourth experiment area includes all three geometric objects used in the study. When the mapping times of the four experimental areas were compared, it was seen that the fastest scenario is achieved within triangular-only objects (9 min 55 sec) and the slowest within square (10 min 43 sec). In terms of measures, the generated map including the triangular prisms is the closest to the actual measures of the simulated area. Accordingly, the mapping error was calculated as 0.171 m 2 per 1 m 2 in an interior made of triangular prisms, and 0.682 m 2 in an interior made of square prisms. The obtained results show that the shapes of the geometric objects directly affect the performance of SLAM.

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Section: Introductionmentioning

confidence: 99%

“… Karto SLAM is a graphics optimization method that uses a highly optimized and noniterative matrix. Karto SLAM algorithm can obtain maps by using a low amount of memory space in static and dynamic environments [9].…”

Section: Introductionmentioning

confidence: 99%

Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo

Aydemir¹,

Tekerek

GÖK

2021

El-Cezeri Fen Ve Mühendislik Dergisi

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“…Graph-based SLAM problem is finally transformed into a problem of finding the optimal solution. In the current mainstream SLAM algorithm based on graph optimization as the basic architecture, the Cartographer algorithm adopts the Scan to Map matching method with small error accumulation and low computational cost, rather than the Scan to Scan matching method that causes rapid error accumulation and high computational cost [10][11] , and the branch-and-bound algorithm is used to accelerate the closed-loop detection [7][12] , which has the advantages of low cumulative error and easy operation [13] . Therefore, this paper uses the Cartographer algorithm as the basis of the fusion reflective column information, and implemented on the ROS (Robot Operating System) platform.…”

mentioning

confidence: 99%

SLAM algorithm based on natural environment and reflective column information

Liu,

Gong

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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Aiming at the stability and accuracy of the mapping and positioning of the Automated Guided Vehicle (AGV) in longdistance driving and complex environments , a post-processing method using graph optimization and filtering is proposed. A terminal optimization algorithm, when this algorithm is optimized, it will give priority to extracting the reflective column information. When the reflective column information is not detected at the front end of SLAM , the system will use the conventional graph optimization algorithm for positioning and mapping. After detecting the reflective column and obtaining the coordinates of the reflective column, the system will switch to the extended Kalman filter. (Extended Kalman Filter, EKF ) algorithm to locate the AGV . It has been verified by experiments that when the system travels long distances through special scenes that are difficult to locate, such as open spaces, complex environments, or frequent changes in the environment, the positioning accuracy and robustness of the SLAM algorithm that alternately uses natural environment information and reflective column information can achieve certain improvements.

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