Fusion LiDAR-Inertial-Encoder data for High-Accuracy SLAM

TL;DR

This paper introduces a sensor fusion method combining LiDAR, inertial, and encoder data within a factor-graph framework to significantly improve SLAM accuracy in challenging, texture-less environments.

Contribution

The paper presents a novel factor-graph-based model that tightly integrates IMU and encoder data, dynamically adjusting weights for enhanced localization accuracy.

Findings

Achieves 26.98% reduction in rotation error

Reduces position error by 67.68%

Demonstrates superior performance over Karto SLAM

Abstract

In the realm of robotics, achieving simultaneous localization and mapping (SLAM) is paramount for autonomous navigation, especially in challenging environments like texture-less structures. This paper proposed a factor-graph-based model that tightly integrates IMU and encoder sensors to enhance positioning in such environments. The system operates by meticulously evaluating the data from each sensor. Based on these evaluations, weights are dynamically adjusted to prioritize the more reliable source of information at any given moment. The robot's state is initialized using IMU data, while the encoder aids motion estimation in long corridors. Discrepancies between the two states are used to correct IMU drift. The effectiveness of this method is demonstrably validated through experimentation. Compared to Karto SLAM, a widely used SLAM algorithm, this approach achieves an improvement of 26.98% in rotation angle error and 67.68% reduction in position error. These results convincingly demonstrate the method's superior accuracy and robustness in texture-less environments.