On sensor model design choices for humanoid robot localization

Abstract

The development of estimation systems based on Kalman filters requires several design choices. Among others, these are the methods used for linearization, coordinate systems for measurement representations, and approximations such as how to handle multiple simultaneous observations per time step. This paper evaluates these different choices with respect to their influence on the system's estimation quality and points out simple yet effective solutions. Camera-based localization for a humanoid robot is chosen as an example application and the localization benefit of different approaches is evaluated using real and simulated feature perceptions. © 2013 Springer-Verlag.