Title: Autonomous Urban Localization and Navigation with Limited Information,
Abstract: Urban environments offer a challenging scenario for autonomous driving.
Globally localizing information, such as a GPS signal, can be unreliable due to
signal shadowing and multipath errors. Detailed a priori maps of the
environment with sufficient information for autonomous navigation typically
require driving the area multiple times to collect large amounts of data,
substantial post-processing on that data to obtain the map, and then
maintaining updates on the map as the environment changes. This paper addresses
the issue of autonomous driving in an urban environment by investigating
algorithms and an architecture to enable fully functional autonomous driving
with limited information. An algorithm to autonomously navigate urban roadways
with little to no reliance on an a priori map or GPS is developed. Localization
is performed with an extended Kalman filter with odometry, compass, and sparse
landmark measurement updates. Navigation is accomplished by a compass-based
navigation control law. Key results from Monte Carlo studies show success rates
of urban navigation under different environmental conditions. Experiments
validate the simulated results and demonstrate that, for given test conditions,
an expected range can be found for a given success rate.