Adaptive Filter Approach for Enhancing Localization Accuracy in Mobile Robots

This article focuses on improving the positional accuracy of mobile robots. Positional accuracy is crucial for robots to reach target points precisely and efficiently. The study combines data from wheel odometry and LiDAR sensors using an adaptive complementary filter. This method aims to merge the advantages of these two data sources while minimizing their disadvantages. LiDAR sensors can map the environment with high accuracy but suffer from issues such as delayed updates due to processing time and reduced reliability in the presence of moving objects. On the other hand, wheel odometry provides real-time data but is prone to errors caused by mechanical wear and slippage, especially on uneven or slippery surfaces. The adaptive complementary filter dynamically weights the reliability of sensor data to address these challenges. In the study, the proposed method continuously optimizes the robot's position and orientation, enhancing motion accuracy. Experimental results demonstrate that during scenarios such as wheel slippage, the system relies more on LiDAR data to maintain positional accuracy, while during straight-line movements, wheel odometry is prioritized for its reliability. This research presents an effective solution for enabling mobile robots to operate more reliably under real-world conditions.