Fusion LiDAR-Inertial-Encoder data for High-Accuracy SLAM
Manh Do Duc, Thanh Nguyen Canh, Minh DoNgoc, Xiem HoangVan
TL;DR
This work tackles SLAM in textureless environments by tightly fusing LiDAR, IMU, and encoder data within a factor-graph framework. It contributes sensor-uncertainty detection, adaptive noise assignment, and reconfigurable optimization to selectively weigh or drop constraints, implemented via iSAM2. Experimental results in Gazebo show substantial gains over GMapping and Karto SLAM, notably a rotation error reduction of $26.98\%$ and a position error reduction of $67.68\%$ relative to Karto, demonstrating improved drift robustness in repetitive corridors. The method advances practical SLAM for AMRs operating in challenging textures and suggests future work on loop closure and real-world testing.
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.