Lidar IMU fusion navigation system for AGVs in smart factories
Haichao Li, XianZhou Wu, Liang Wang, Xianke Jian, Songming Liu, Zeyu Chen, Senyang Chen, Ezzeddine Touti
TL;DR
This paper introduces a new method for improving AGV navigation in smart factories by fusing LiDAR and IMU data more effectively.
Contribution
The novel Screened Inertial Data Fusion Method (SIDFM) reduces navigation errors by systematically screening and fusing LiDAR and IMU data.
Findings
SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration.
Positioning errors are significantly decreased, improving AGV stability and precision.
SIDFM outperforms baseline methods in dynamic manufacturing environments.
Abstract
Automated Guided Vehicles (AGVs) are vital to smart factories, enabling autonomous and efficient material transport. However, precise navigation is challenging because LiDAR provides high-dimensional, dynamic spatial data, while Inertial Measurement Unit (IMU) signals are often intermittent, leading to inconsistencies and navigation drift. This work proposes the Screened Inertial Data Fusion Method (SIDFM), a novel framework that systematically screens LiDAR data using a minimal differential function and fuses it with IMU intervals through linear regression learning. The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. Results show that SIDFM reduces…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Inertial Sensor and Navigation · Astronomical Observations and Instrumentation