Optimization of Gmapping Algorithm Based on Fusion of IMU and Odometer in Multiple Scenarios

This paper presents an optimization method for the Gmapping algorithm based on the fusion of Inertial Measurement Unit (IMU) and odometer data to improve localization accuracy in indoor robot navigation. Traditional Gmapping algorithms are heavily based on odometer data, which can suffer from cumulative errors that degrade localization precision. To mitigate these errors, this paper introduces the IMU data. It uses an unscented Kalman Filter (UKF) for sensor data fusion, thereby correcting odometer errors caused by wheel slip, installation errors, and uneven terrain. Through both simulation and realworld experiments, the proposed method significantly enhances mapping accuracy and localization stability in multiple indoor environments, including hospitals and factories. The experimental results demonstrate that the fusion of IMU and odometer data effectively reduces errors, improving the reliability and practicality of indoor Simultaneous Location and Mapping (SLAM) systems.