Road Detection and Tracking for Autonomous Mobile Robots
Tommy Chang, Tsai Hong Hong, Michael O. Shneier, C E. Rasmussen
As part of the Army's Demo III project, a sensor-based system has been developed to identify roads and to enable a mobile robot to drive along them. A ladar sensor, which produces range images, and a color camera are used in conjunction to locate the road surface and its boundaries. Sensing is used to constantly update an internal world model of the road surface. The world model is used to predict the future position of the road and to focus the attention of the sensors on the relevant regions in their respective images. The world model also determines the most suitable algorithm for the locating and tracking road features in the images based on the current task and sensing information. The planner uses information from the world model to determine the best path for the vehicle along the road. Several different algorithms have been developed and tested on a diverse set of road sequences. The road types include some paved roads with lanes, but most of the sequences are of unpaved roads, including dirt and gravel roads. The algorithms compute various features of the road images including smoothness in the world model map and in the range domain, and color features and texture in the color domain. Performance in road detection and tracking are described and examples are shown of the system in action.
Proceeding of the SPIE 16th Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Controls
April 1-5, 2002
Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Controls
color, focus of attend, ladar, Mobile robot, road detection, road/lane following, sensor-based system, windowing, world model
, , T.
, Shneier, M.
and Rasmussen, C.
Road Detection and Tracking for Autonomous Mobile Robots, Proceeding of the SPIE 16th Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Controls, Orlando, FL, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821688
(Accessed December 8, 2023)