  Our goal is to send legged robots into challenging, unstructured terrains
that wheeled systems cannot traverse. Moreover, precise estimation of the
robot's position and orientation in rough terrain is especially difficult. To
address this problem, we introduce a new state estimation algorithm which we
term Contact-Centric Leg Odometry (COCLO). This new estimator uses a Square
Root Unscented Kalman Filter (SR-UKF) to fuse multiple proprioceptive sensors
available on a legged robot. In contrast to IMU-centric filtering approaches,
COCLO formulates prediction and measurement models according to the contact
status of legs. Additionally, COCLO has an indirect measurement model using
joint velocities to estimate the robot body velocity. In rough terrain, when
IMUs suffer from large amounts of noise, COCLO's contact-centric approach
outperforms previous IMU-centric methods. To demonstrate improved state
estimation accuracy, we compare COCLO with Visual Inertial Navigation System
(VINS), a state-of-the-art visual inertial odometry, in three different
environments: flat ground, ramps, and stairs. COCLO achieves better estimation
precision than VINS in all three environments and is robust to unstable motion.
Finally, we also show that COCLO and a modified VINS can work in tandem to
improve each other's performance.
