ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information...

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Bibliographic Details
Published inMachines (Basel) Vol. 10; no. 7; p. 519
Main Authors Somlyai, László, Vámossy, Zoltán
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2022
Subjects
Acceptable noise levels
Accuracy
Algorithms
Cameras
Electronic devices
Global positioning systems
GPS
ISVD
Localization
Location-based systems
Mapping
Matching
Methods
mobile robot
Mobile robots
Outliers (statistics)
Point pairs
robot navigation
Robots
Route planning
Sensors
Simultaneous localization and mapping
SVD
Unmanned aerial vehicles
Hungary
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Summary:In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information can also be gathered along with ordinary RGB color data. Using these RGB-D sensors, two- and three-dimensional point clouds can be recorded from the mobile devices, which provide additional information for localization and mapping. The method of matching point clouds during the movement of the device is essential: reducing noise while having an acceptable processing time is crucial for a real-life application. In this paper, we present a novel ISVD-based method for displacement estimation, using key points detected by SURF and ORB feature detectors. The ISVD algorithm is a fitting procedure based on SVD resolution, which removes outliers from the point clouds to be fitted in several steps. The developed method removes these outlying points in several steps, in each iteration examining the relative error of the point pairs and then progressively reducing the maximum error for the next matching step. An advantage over relevant methods is that this method always gives the same result, as no random steps are included.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines10070519