<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Simultaneous localization and map building of mobile robots is an important research direction in robotics, and multi-sensor fusion is one of the key technologies for mobile robots to achieve autonomous navigation. In this paper, firstly, based on the framework of the SLAM system, we analyze the extraction of FAST feature points and BRIEF feature descriptor matching and propose the CSD-ORB algorithm. Secondly, the FKCS-LD algorithm based on the fusion map key frame and center selection strategy is proposed. The suitable multi-sensor fusion SLAM system is selected to build the back-end optimization algorithm based on the bit-pose map. Finally, we complete the comparison experiments of the multi-sensor fusion SLAM system on standard data sets, robot hardware assembly and software environment construction, and actual field testing. The experimental data show that the trajectory calculation accuracy can be further improved by using local map tracking, i.e., the current frame and local keyframe to calculate the bit pose, in which the maximum error is 6 cm, the root mean square error is reduced to 3.6 cm, the maximum value of local optimization is 0.66034, the minimum value is 0.146232, the average is 0.32812, and the median is 0.33023. The experiment proves that the multi-sensor fusion SLAM system can achieve centimeter-level accuracy and has global consistency and real-time performance, and can effectively complete the task of simultaneous robot positioning and map building.</p>
</abstract>

Simultaneous localization and map building of mobile robots is an important research direction in robotics, and multi-sensor fusion is one of the key technologies for mobile robots to achieve autonomous navigation. In this paper, firstly, based on the framework of the SLAM system, we analyze the extraction of FAST feature points and BRIEF feature descriptor matching and propose the CSD-ORB algorithm. Secondly, the FKCS-LD algorithm based on the fusion map key frame and center selection strategy is proposed. The suitable multi-sensor fusion SLAM system is selected to build the back-end optimization algorithm based on the bit-pose map. Finally, we complete the comparison experiments of the multi-sensor fusion SLAM system on standard data sets, robot hardware assembly and software environment construction, and actual field testing. The experimental data show that the trajectory calculation accuracy can be further improved by using local map tracking, i.e., the current frame and local keyframe to calculate the bit pose, in which the maximum error is 6 cm, the root mean square error is reduced to 3.6 cm, the maximum value of local optimization is 0.66034, the minimum value is 0.146232, the average is 0.32812, and the median is 0.33023. The experiment proves that the multi-sensor fusion SLAM system can achieve centimeter-level accuracy and has global consistency and real-time performance, and can effectively complete the task of simultaneous robot positioning and map building.

Simultaneous localization and mapping of mobile robots with multi-sensor fusion

Henan Vocational University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Simultaneous localization and mapping of mobile robots with multi-sensor fusion"}

Simultaneous localization and map building of mobile robots is an important research direction in robotics, and multi-sensor fusion is one of the key...

Simultaneous localization and mapping of mobile robots with multi-sensor fusion

Published Online: Oct 04, 2023

Page range: -

Received: Dec 23, 2022

Accepted: Apr 14, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00488

Keywords
Multi-sensor fusion, SLAM technology, CSD-ORB algorithm, FKCS-LD algorithm, Mobile robot

© 2023 Kui Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Simultaneous localization and mapping of mobile robots with multi-sensor fusion

Published Online: Oct 04, 2023

Page range: -

Received: Dec 23, 2022

Accepted: Apr 14, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00488

KeywordsMulti-sensor fusion, SLAM technology, CSD-ORB algorithm, FKCS-LD algorithm, Mobile robot

© 2023 Kui Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Multi-sensor fusion, SLAM technology, CSD-ORB algorithm, FKCS-LD algorithm, Mobile robot