Autonomous Urban Localization and Navigation with Limited Information

TL;DR

This paper develops algorithms and an architecture enabling autonomous urban driving with limited information, such as unreliable GPS and sparse maps, by combining localization and navigation techniques validated through simulations and experiments.

Contribution

It introduces a novel approach for urban autonomous driving that does not rely on detailed pre-mapped data or GPS, using a Kalman filter and compass-based control.

Findings

High success rates in simulated urban navigation under various conditions

Validation of algorithms through experiments matching simulation results

Range estimates for given success rates in urban driving scenarios

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.