6 accepted papers
Legged robot navigation in unstructured and slippery terrains depends heavily on the ability to accurately identify the quality of contact between the robot's feet and the ground. Contact state estimation is regarded as a challenging problem and is typically addressed by exploiting force measurement…
In this article, we propose a deep learning frame-work that provides a unified approach to the problem of leg contact detection in humanoid robot walking gaits. Our formulation accomplishes to accurately and robustly estimate the contact state probability for each leg (i.e., stable or slip/no contac…
In the current paper we investigate the challenges of localizing walking humanoid robots using Visual SLAM (VSLAM). We propose a novel dense RGB-D SLAM framework that seamlessly integrates with the dynamic state of a humanoid, to provide real-time localization and dense mapping of its surroundings.…
Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized for Visual Odometry (VO) and LiDAR Odometry (LO). Unfortunately, such measurements commonly suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion a…
Contact detection is an important topic in contemporary humanoid robotic research. Up to date control and state estimation schemes readily assume that feet contact status is known in advance. In this work, we elaborate on a broader question: in which gait phase is the robot currently in? We introduc…
This letter presents a novel cascade state estimation framework for the three-dimensional (3-D) center of mass (CoM) estimation of walking humanoid robots. The proposed framework, called State Estimation RObot Walking (SEROW), fuses effectively joint encoder, inertial, feet pressure, and visual odom