Robotics Institute Thesis Proposal: Planning with Uncertainty in Position Using High-Resolution Maps

Author:
Juan Pablo Gonzalez
Robotics Institute
Carnegie Mellon University

Abstract:
Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task can be extremely difficult if no prior information is available, and would be trivial if perfect prior information existed. In practice prior maps are usually available, but their quality and resolution varies significantly.

When accurate, high-resolution prior maps are available and the position of the robot is precisely known, many existing approaches can reliably perform the navigation task for an autonomous robot. However, if the position of the robot is not precisely known, most existing approaches would fail or would have to discard the prior map and perform the much harder task of navigating without prior information.

Most outdoor robotic platforms have two ways of determining their position: a dead-reckoning system and Global Position Systems (GPS). The dead reckoning system provides a locally accurate and locally consistent estimate that drifts slowly, and the GPS provides globally accurate estimate that does not drift, but is not necessarily locally consistent. A Kalman filter usually combines these two estimates to provide an estimate that has the best of both position estimates.

While for many scenarios this combination suffices, there are many others in which GPS is not available, or its reliability is compromised by different types of interference such as mountains, buildings, foliage or jamming. In these cases, the only position estimate available is that of the dead-reckoning system which drifts with time and does not provide a position estimate accurate enough for most navigation approaches.

This proposal addresses the problem of planning with uncertainty in position using high-resolution maps. The objective is to be able to reliably navigate distances of up to one kilometer without GPS through the use of accurate, high resolution prior maps and a good dead-reckoning system. Different approaches to the problem are analyzed, depending on the types of landmarks available, the quality of the map and the quality of the perception system.

Further Details:
A copy of the thesis proposal document can be found at http://www.ri.cmu.edu/pubs/pub_5571.html.