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.

翻译：在机器人学领域，实现同步定位与建图（SLAM）对于自主导航至关重要，尤其在纹理缺失的结构等挑战性环境中。本文提出一种基于因子图的模型，通过紧密融合IMU与编码器传感器来提升此类环境中的定位性能。该系统通过精细评估各传感器数据来运行，并基于评估结果动态调整权重，以优先采用当前时刻更可靠的信息源。机器人状态使用IMU数据进行初始化，而编码器则在长走廊场景中辅助运动估计。两种状态间的差异被用于校正IMU漂移。实验充分验证了该方法的有效性：与广泛使用的Karto SLAM算法相比，本方法在旋转角度误差上实现了26.98%的改善，位置误差降低了67.68%。这些结果有力证明了该方法在无纹理环境中具有更优的精度与鲁棒性。