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This paper introduces an Interacting Multiple Model (IMM)-based proprioceptive odometry framework to improve state estimation for legged robots, which often suffer from drift due to limited observability and reliance on idealized point-contact assumptions. The IMM approach incorporates multiple contact hypotheses, enabling online mode switching and probabilistic fusion to better handle varying contact conditions. Results from simulations and real-world experiments show that the proposed method achieves higher pose estimation accuracy compared to existing proprioceptive odometry techniques, without sacrificing computational efficiency.
Legged robots can now navigate more accurately using only internal sensors, even with imperfect foot contact, thanks to a new probabilistic method that dynamically adapts to different contact scenarios.
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.
