A New Approach to Real-Time Odometry Calibration Using an Adaptive Particle Filter Design

This paper presents a novel calibration system for odometric sensors using an Adaptive Particle Filter (Adaptive-PF) to achieve high precision pose estimation and improve localization in wheeled mobile robots. The system reduces for intrinsic systematic errors in the odometric sensor by adjusting its parameters in realtime. Likewise, a comparative analysis of resampling methods —multinomial, stratified, systematic, and residual resampling— is conducted to evaluate their impact on calibration performance. The system validation is demonstrated by its implementation in an autonomous wheelchair, where the localization module integrates wheel encoders, an Inertial Measurement Unit (IMU), and a LIDAR sensor, providing robust navigation in dynamic environments. Experimental results demonstrate that systematic approach and resampling based on the effective number of particles ($N_{eff}$) yield the best performance. Additionally, the system dynamically adjusts prediction error based on the differences between LIDAR and odometry data. It also adapts the number of particles according to the dispersion and uncertainty, optimizing computational time without sacrificing accuracy. The proposed system outperforms another well-known method, namely the DKF (Dual Kalman Filter). Consequently, this research introduces a new Adaptive-PF for odometric parameter calibration under changing conditions.