Magnetometer-Aided Proprioceptive Factor Graph for Legged Robot Localization

Abstract

The state-of-the-art legged robot localization algorithms typically rely on both proprioceptive data and exteroceptive systems such as LiDAR, vision, and GNSS, fused using smoothing or filtering methods like Factor Graph Optimization or the Extended Kalman Filter (EKF). However, such methods are limited to environments with distinct features or robots containing specific hardware, specifically high quality GNSS receiver. This paper presents a Factor-graph-based localization framework that uses only proprioceptive sensing IMU and motor encoder kinematics - augmented with magnetometer yaw observations. The method is implemented on a Unitree Go2 quadrupedal robot in NVIDIA Isaac Sim and evaluated across five trajectories. The results show that the proposed approach achieves stable, drift-minimized localization, with a maximum deviation of less than 4% of the total trajectory length.