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.

Expanding Navigation Systems by Integrating It with Advanced Technologies

“…Urban environments offer a challenging scenario for autonomous driving [6]. The proposed solution allows autonomously navigate urban roadways with minimum a priori map or GPS.…”

Estimation and navigation methods with limited information for autonomous urban driving

“…This system diagram contains familiar elements in autonomous driving, such as steering and speed controllers, an object tracker, and a path generator. 30 However, the pose estimator, navigation algorithm, and static scene estimator are updated from their typical form to face the difficulties associated with the lack of map information and GPS measurements. The proposed approach relies on local sensors to allow for real-time control of the vehicle (ie, staying in a lane).…”

“…The system architecture developed for this study is shown in Figure . This system diagram contains familiar elements in autonomous driving, such as steering and speed controllers, an object tracker, and a path generator . However, the pose estimator, navigation algorithm, and static scene estimator are updated from their typical form to face the difficulties associated with the lack of map information and GPS measurements.…”