State Estimation for Legged Robots – Kinematics, Inertial Sensing, and Computer Vision

The primary aim of this thesis is to endow legged robots with a reliable sense of ego-motion. Just like humans or other legged beings, many legged robots require estimates of their posture and velocity in order to keep balance and move through the environment. The estimates need to exhibit both sufficiently high bandwidth and accuracy in order to allow for a controlled execution of these tasks. Furthermore, due to this dependency, failures of the state estimation may quickly lead to damaging of the robot or its surroundings, which emphasizes the importance of the reliability of the employed estimation algorithms. The associated research question may be formulated as finding an appropriate combination of sensor modalities and state estimation algorithms such that the ego-motion can reliably and accurately be estimated with financially and computationally reasonable costs. Furthermore, the state estimation should not limit the capabilities of the robot and thus the use of restrictive assumptions such as a horizontal terrain, specific gait patterns, or the availability of external sensing is undesirable. In a first step the focus will be set on proprioceptive sensing in order to keep the data processing simple and thus keep time delays small and avoid additional error sources. In contrast to other types of robots, legged robots interact with their environment through intermittent foot-ground contacts. Assuming stationary ground contacts and measurable forward kinematics, this interaction can provide the legged robot with a very valuable source of information. Since most modern robotic platforms are also equipped with inertial sensing devices, the combination of inertial and kinematic data becomes an apparent approach for solving the state estimation problem for legged robots. However, careful modeling and sensor fusion design are prerequisites for achieving good performance. Concurrently, further challenges such as online calibration of Inertial Measurement Unit (IMU) biases, handling of slipping feet, or mitigating numerical inconsistencies have to be taken care of before truly reliable egomotion estimation can be attained. In order to tackle these challenges we propose to co-estimate the foothold locations within a Kalman filter framework. We thereby also circumvent the use of restrictive assumptions such as a flat environment or a fixed gait pattern. In a second part we investigate the use of exteroceptive sensing in order to reduce drift that occurs when employing proprioception only. We focus on the use of cameras due to the rich information they provide while exhibiting low weight and low power consumption. In order to achieve high robustness, it is important to incorporate inertial data during visual processing for reducing vision-only related failure modes, such as caused by fast motion or missing texture. Furthermore, the handling of fast motion during which the scene may pass rapidly trough the field of view, is improved if visual information extraction occurs from a feature’s second observation onwards. Ideally, the handling of visual features is kept simple such that no cumbersome initialization