Research on Factor Graph-based SLAM Localization Algorithm for Multi-source Sensor Fusion

A multi-sensor tightly coupled localization algorithm based on a factor graph is proposed to address the challenges of low single-sensor localization accuracy and insufficient robustness of mobile robots in outdoor environments. The algorithm incorporates Inertial Measurement Unit (IMU) data at the...

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Bibliographic Details
Published in2024 IEEE International Conference on Cognitive Computing and Complex Data (ICCD) pp. 294 - 301
Main Authors Ma, Tao, Zhu, Liucun, Wu, Xiao, Chen, Sijie, Wang, Nanxiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.09.2024
Subjects
Accuracy
Estimation
factor graph
Global navigation satellite system
Laser radar
localization
Location awareness
multi-sensor
Point cloud compression
Robustness
Sensor fusion
Simultaneous localization and mapping
tight coupling
Trajectory
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Summary:A multi-sensor tightly coupled localization algorithm based on a factor graph is proposed to address the challenges of low single-sensor localization accuracy and insufficient robustness of mobile robots in outdoor environments. The algorithm incorporates Inertial Measurement Unit (IMU) data at the front end for point cloud de-distortion. It utilizes the IMU pre-integration result as the initial position to enhance point cloud alignment accuracy, thereby improving the overall position estimation of the robot. The back end constructs the IMU pre-integration factor, Lidar odometry factor, Global Navigation Satellite System (GNSS) factor, and loop closure detection factor through a factor graph, and outputs the robot's state information through incremental optimization. Test results on the M2DGR dataset demonstrate that the algorithm significantly enhances localization accuracy and robustness in both closed-loop and open-loop outdoor scenarios.
DOI:10.1109/ICCD62811.2024.10843557