A visual navigation system

A modular system architecture has been developed to support visual navigation by an autonomous land vehicle. The system consists of vision modules performing image processing, 3-D shape recovery, and rule-based reasoning, as well as modules for planning, navigating and piloting. The system runs in two distinct modes, bootstrap and feed-forward. The bootstrap mode requires analysis of entire images in order to find and model the objects of interest in the scene (e. g. roads). In the feed-forward mode (while the vehicle is moving), attention is focused on small parts of the visual field as determined by prior views of the scene, in order to continue to track and model the objects of interest. We have decomposed general navigational tasks into three categories, all of which contribute to planning a vehicle path. They are called long, intermediate and short range navigation, reflecting the scale to which they apply. We have implemented the system as a set of concurrent, communicating modules and use it to drive a camera (carried by a robot arm) over a scale model road network on a terrain board.

Authors

• Allen M. Waxman
• Jacqueline Le Moigne
• Larry S. Davis
• Eli Liang
• T. Siddalingaiah

Keywords

• Navigation
• Feedforward systems
• Layout
• Roads
• Land vehicles
• Machine vision
• Image processing
• Shape
• Process planning
• Image analysis
• Visual Navigation System
• 3D Reconstruction
• System Architecture
• Road Network
• Autonomous Vehicles
• Object Of Interest
• Robotic Arm
• Modular System
• Navigation Task
• Visual System
• Flow Control
• Object Location
• Path Planning
• Road Map
• Image Domain
• Linear Features
• Vehicle Position
• Obstacle Avoidance
• Road Boundary
• Linear Encoder
• Space Decomposition
• Dead Reckoning
• Intermediate Range
• Processing Mode
• Part Of The Road
• CPU Time In Seconds
• Real Vehicle
• Visual Resources