Two-Dimensional-Simultaneous Localisation and Mapping Study Based on Factor Graph Elimination Optimisation

Xinzhao Wu, Peiqing Li (), Qipeng Li and Zhuoran Li
Additional contact information
Xinzhao Wu: School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Peiqing Li: School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Qipeng Li: School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Zhuoran Li: Faculty of Information Technology, City University Malaysia, Petaling Jaya 46100, Malaysia

Sustainability, 2023, vol. 15, issue 2, 1-20

Abstract: A robust multi-sensor fusion simultaneous localization and mapping (SLAM) algorithm for complex road surfaces is proposed to improve recognition accuracy and reduce system memory occupation, aiming to enhance the computational efficiency of light detection and ranging in complex environments. First, a weighted signed distance function (W-SDF) map-based SLAM method is proposed. It uses a W-SDF map to capture the environment with less accuracy than the raster size but with high localization accuracy. The Levenberg–Marquardt method is used to solve the scan-matching problem in laser SLAM; it effectively alleviates the limitations of the Gaussian–Newton method that may lead to insufficient local accuracy, and reduces localisation errors. Second, ground constraint factors are added to the factor graph, and a multi-sensor fusion localisation algorithm is proposed based on factor graph elimination optimisation. A sliding window is added to the chain factor graph model to retain the historical state information within the window and avoid high-dimensional matrix operations. An elimination algorithm is introduced to transform the factor graph into a Bayesian network to marginalize the historical states and reduce the matrix dimensionality, thereby improving the algorithm localisation accuracy and reducing the memory occupation. Finally, the proposed algorithm is compared and validated with two traditional algorithms based on an unmanned cart. Experiments show that the proposed algorithm reduces memory consumption and improves localisation accuracy compared to the Hector algorithm and Cartographer algorithm, has good performance in terms of accuracy, reliability and computational efficiency in complex pavement environments, and is better utilised in practical environments.

Keywords: simultaneous localisation and mapping; scan-matching algorithm; multi-sensor fusion; factor graph optimisation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/2/1172/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/2/1172/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:2:p:1172-:d:1028856

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().