实验验证：使用扩展卡尔曼滤波算法的自动驾驶汽车感知和定位系统

Pub Date : 2024-01-01 DOI:10.2478/ijssis-2024-0002

B. L. Widjiantoro, K. Indriawati, T. S. N. Alexander Buyung, Kadek Dwi Wahyuadnyana

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引用次数: 0

摘要

本研究通过使用扩展卡尔曼滤波器整合 MPU6050 传感器和编码器数据的实验，验证了 EKF-SLAM 在室内自动驾驶汽车中的应用。实际测试表明，EKF-SLAM 在 X 轴和 Y 轴上的误差仅为 1%和 3%，达到了很高的精度。RPLiDAR A1M8 用于测绘，通过 RViz-ROS 生成可视化的精确地图。这项研究证明了 EKF-SLAM 在实际应用场景中的新颖性和实用性，展示了前所未有的有效性和精确性。

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Experimental Validation: Perception and Localization Systems for Autonomous Vehicles using the Extended Kalman Filter Algorithm

This study validates EKF-SLAM for indoor autonomous vehicles by experimentally integrating the MPU6050 sensor and encoder data using an extended Kalman filter. Real-world tests show significant improvements, achieving high accuracy with just 1% and 3% errors in the X and Y axes. RPLiDAR A1M8 is utilized for mapping, producing accurate maps visualized through RViz-ROS. The research demonstrates the novelty and practical utility of EKF-SLAM in real-world scenarios, showcasing unprecedented effectiveness and precision.

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