Enhancing LiDAR Odometry with Adaptive Integration of Wheel Odometry and IMU in Environments with Few Geometric Features

Autonomous robots have been employed to improve safety and efficiency in sectors such as mining and industry, particularly in inspection tasks within confined environments like tunnels and underground galleries. However, these scenarios pose significant challenges for robot localization and mapping, mainly due to the lack of geometrical references. Although LiDAR-based SLAM techniques are widely used, they tend to fail under such conditions. This paper proposes a sensor fusion approach that combines wheel odometry, LiDAR, and IMU data using an Extended Kalman Filter. The methodology is validated in a simulated environment with long corridors and asymmetric rooms, where traditional SLAM algorithms show limitations. The results demonstrate improved odometry accuracy compared to both a baseline strategy without the EKF and our previous EKF-LOAM approach, as well as enhanced navigation reliability in geometrically sparse environments.