Huang Guohao, Huang Haibin, Zhai Yaning, Tang Guohao, Zhang Ling, Gao Xingyu, Huang Yang, Ge Guoping
School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
School of Artificial Intelligence, Guangxi Minzu University, Nanning 541004, China.
Sensors (Basel). 2024 Nov 28;24(23):7619. doi: 10.3390/s24237619.
This paper investigates the odometry drift problem in differential-drive indoor mobile robots and proposes a multi-sensor fusion approach utilizing a Fuzzy Inference System (FIS) within a Wheel-Inertial-Visual Odometry (WIVO) framework to optimize the 6-DoF localization of the robot in unstructured scenes. The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, which fail to adapt effectively to complex kinematic characteristics and visual observation challenges such as varying lighting conditions and unstructured scenes in dynamic environments. The fusion filter gains in FIS-IESKF are adaptively adjusted for noise predictions, optimizing the rule parameters of the fuzzy inference process. Experimental results demonstrate that the proposed method effectively enhances the localization accuracy and system robustness of differential-drive indoor mobile robots in dynamically changing movements and environments.
本文研究了差速驱动室内移动机器人的里程计漂移问题，并提出了一种多传感器融合方法，该方法在轮式惯性视觉里程计（WIVO）框架内利用模糊推理系统（FIS）来优化机器人在非结构化场景中的六自由度定位。开发了多传感器融合系统的结构和原理，并结合迭代误差状态卡尔曼滤波器（IESKF）以提高精度。将FIS与IESKF集成，以解决传统固定协方差矩阵在过程和观测噪声方面的局限性，这些局限性无法有效适应复杂的运动学特征以及动态环境中诸如光照条件变化和非结构化场景等视觉观测挑战。FIS-IESKF中的融合滤波器增益针对噪声预测进行自适应调整，优化模糊推理过程的规则参数。实验结果表明，该方法有效地提高了差速驱动室内移动机器人在动态变化的运动和环境中的定位精度和系统鲁棒性。
