Simultaneous local and global state estimation for robotic navigation

Recent applications of robotics often demand two types of spatial awareness: 1) A fine-grained description of the robot's immediate surroundings for obstacle avoidance and planning, and 2) Knowledge of the robot's position in a large-scale global coordinate frame such as that provided by GPS. Although managing information at both of these scales is often essential to the robot's purpose, each scale has different requirements in terms of state representation and handling of uncertainty. In such a scenario, it can be tempting to pick either a body-centric coordinate frame or a globally fixed coordinate frame for all state representation. Although both choices have advantages, we show that neither is ideal for a system that must handle both global and local data. This paper describes an alternative design: a third coordinate frame that stays fixed to the local environment over short time-scales, but can vary with respect to the global frame. Careful management of uncertainty in this local coordinate frame makes it well-suited for simultaneously representing both locally and globally derived data, greatly simplifying system design and improving robustness. We describe the implementation of this coordinate frame and its properties when measuring uncertainty, and show the results of applying this approach to our 2007 DARPA Urban Challenge vehicle.