融合超声定位与轮式里程计，提升室内机器人定位精度
Mobile Robot Localization via Indoor Positioning System and Odometry Fusion
- 用扩展卡尔曼滤波融合超声定位与轮子里程计数据
- 相比单独使用任一系统，轨迹跟踪误差显著降低
- 适合需要高精度定位的室内移动机器人应用
精确的定位对移动机器人在室内环境中的有效运行至关重要。本文提出一种综合方法，通过传感器融合技术将基于超声的室内定位系统（IPS）与轮式里程计数据相结合。该融合方法充分利用了IPS和轮式里程计各自的优点，弥补了各自存在的局限性。采用扩展卡尔曼滤波（EKF）融合算法，整合来自IPS传感器和机器人轮式里程计的数据，提供稳健可靠的定位方案。在受控室内环境中进行的大量实验表明，融合后的定位系统在准确性和精度上显著优于单一系统。结果表明，基于EKF的方法有效降低了因轮子打滑和传感器噪声带来的误差，显著提升了轨迹跟踪性能。
原文摘要 · Abstract (English)
Accurate localization is crucial for effectively operating mobile robots in indoor environments. This paper presents a comprehensive approach to mobile robot localization by integrating an ultrasound-based indoor positioning system (IPS) with wheel odometry data via sensor fusion techniques. The fusion methodology leverages the strengths of both IPS and wheel odometry, compensating for the individual limitations of each method. The Extended Kalman Filter (EKF) fusion method combines the data from the IPS sensors and the robot's wheel odometry, providing a robust and reliable localization solution. Extensive experiments in a controlled indoor environment reveal that the fusion-based localization system significantly enhances accuracy and precision compared to standalone systems. The results demonstrate significant improvements in trajectory tracking, with the EKF-based approach reducing errors associated with wheel slippage and sensor noise.
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