This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system-denied and highly dynamic environments with poor a priori information. C 2008 Wiley Periodicals, Inc.

This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kino-dynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a Rapidly-exploring Randomized Trees (RRT) algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message-passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in GPS-denied and highly dynamic environments with poor a priori information.

Robust egomotion recovery for extended camera excursions has long been a challenge for machine vision researchers. Existing algorithms handle spatially limited environments and tend to consume prohibitive computational resources with increasing excursion time and distance.We describe an egomotion estimation algorithm that takes as input a coarse 3D model of an environment, and an omnidirectional video sequence captured within the environment, and produces as output a reconstruction of the camera's 6-DOF egomotion expressed in the coordinates of the input model. The principal novelty of our method is a robust matching algorithm that associates 2D edges from the video with 3D line segments from the input model.Our system handles 3-DOF and 6-DOF camera excursions of hundreds of meters within real, cluttered environments. It uses a novel prior visibility analysis to speed initialization and dramatically accelerate image-to-model matching. We demonstrate the method's operation, and qualitatively and quantitatively evaluate its performance, on both synthetic and real image sequences.

Abstract-Precise calibration of camera intrinsic and extrinsic parameters, while often useful, is difficult to obtain during field operation and presents scaling issues for multi-robot systems. We demonstrate a vision-based approach to navigation that does not depend on traditional camera calibration, and present an algorithm for guiding a robot through a previously traversed environment using a set of uncalibrated cameras mounted on the robot.On the first excursion through an environment, the system builds a topological representation of the robot's exploration path, encoded as a place graph. On subsequent navigation missions, the method localizes the robot within the graph and provides robust guidance to a specified destination. We combine this method with reactive collision avoidance to obtain a system able to navigate the robot safely and reliably through the environment. We validate our approach with ground-truth experiments and demonstrate the method on a small ground rover navigating through several dynamic environments.

We present a vision-based