Multi-IMU Proprioceptive Odometry for Legged Robots

This paper presents a novel, low-cost proprioceptive sensing solution for legged robots with point feet to achieve accurate low-drift long-term position and velocity estimation. In addition to conventional sensors, including one body Inertial Measurement Unit (IMU) and joint encoders, we attach an additional IMU to each calf link of the robot just above the foot. An extended Kalman filter is used to fuse data from all sensors to estimate the robot’s body and foot positions in the world frame. Using the additional IMUs, the filter is able to reliably determine foot contact modes and detect foot slips without tactile or pressure-based foot contact sensors. This sensing solution is validated in various hardware experiments, which confirm that it can reduce position drift by nearly an order of magnitude compared to conventional approaches with only a very modest increase in hardware and computational costs.

Recommended citation: Yang S, Zhang Z, Bokser B, et al. Multi-IMU Proprioceptive Odometry for Legged Robots[C]//2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023: 774-779.