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This paper introduces an interacting multiple model (IMM)-based proprioceptive odometry framework for legged robots to improve state estimation accuracy. The framework incorporates multiple contact hypotheses and uses online mode switching and probabilistic fusion to handle varying contact conditions. Results from simulations and real-world experiments show the proposed method outperforms existing proprioceptive odometry methods in pose estimation accuracy while maintaining comparable computational efficiency.
Legged robots can navigate more accurately in challenging terrains by probabilistically fusing multiple contact models, even when traditional point-contact assumptions fail.
State estimation for legged robots remains challenging because legged odometry generally suffers from limited observability and therefore depends critically on measurement constraints to suppress drift. When exteroceptive sensors are unreliable or degraded, such constraints are mainly derived from proprioceptive measurements, particularly contact-related leg kinematics information. However, most existing proprioceptive odometry methods rely on an idealized point-contact assumption, which is often violated during real locomotion. Consequently, the effectiveness of proprioceptive constraints may be significantly reduced, resulting in degraded estimation accuracy. To address these limitations, we propose an interacting multiple model (IMM)-based proprioceptive odometry framework for legged robots. By incorporating multiple contact hypotheses within a unified probabilistic framework, the proposed method enables online mode switching and probabilistic fusion under varying contact conditions. Extensive simulations and real-world experiments demonstrate that the proposed method achieves superior pose estimation accuracy over state-of-the-art methods while maintaining comparable computational efficiency.