Vision SLAM algorithm for wheeled robots integrating multiple sensors.
PLoS One
; 19(3): e0301189, 2024.
Article
in En
| MEDLINE
| ID: mdl-38547130
ABSTRACT
Wheeled robots play a crucial role in driving the autonomy and intelligence of robotics. However, they often encounter challenges such as tracking loss and poor real-time performance in low-texture environments. In response to these issues, this research proposes a real-time localization and mapping algorithm based on the fusion of multiple features, utilizing point, line, surface, and matrix decomposition characteristics. Building upon this foundation, the algorithm integrates multiple sensors to design a vision-based real-time localization and mapping algorithm for wheeled robots. The study concludes with experimental validation on a two-wheeled robot platform. The results indicated that the multi-feature fusion algorithm achieved the highest average accuracy in both conventional indoor datasets (84.57%) and sparse-feature indoor datasets (82.37%). In indoor scenarios, the vision-based algorithm integrating multiple sensors achieved an average accuracy of 85.4% with a processing time of 64.4 ms. In outdoor scenarios, the proposed algorithm exhibited a 14.51% accuracy improvement over a vision-based algorithm without closed-loop detection. In summary, the proposed method demonstrated outstanding accuracy and real-time performance, exhibiting favorable application effects across various practical scenarios.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Robotics
Language:
En
Journal:
PLoS One
Journal subject:
CIENCIA
/
MEDICINA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA