Selecting stable image features for robot localization using stereo

To navigate and recognize where it is, a mobile robot must be able to identify its current location. In an unknown initial position, a robot needs to refer to its environment to determine its location in an external coordinate system. Even with a known initial position, drift in odometry causes the estimated position to deviate from the correct position, requiring correction. We show how to find landmarks without models. We use dense stereo data from our mobile robot's trinocular system to discover image regions that will be stable over widely differing viewpoints. We find image brightness "corners" in images and select those that do not straddle depth discontinuities in the stereo depth data. Selecting corners only in regions of nearly planar stereo data results in landmarks that can be seen in images taken from different viewpoints.

Authors

• James J. Little
• Jiping Lu
• Don Ray Murray

Keywords

• Robot localization
• Mobile robots
• Robot kinematics
• Navigation
• Robot sensing systems
• Intelligent robots
• Layout
• Image storage
• Computer science
• Brightness
• Coordinate System
• Correct Position
• Depth Data
• Mobile Robot
• Odometry
• Reference Image
• Local Image
• Total Error
• Image Point
• Geometric Information
• Normal Error
• Coordinate Frame
• Obstacle Avoidance
• Stereopsis
• Local Geometry
• Robot Navigation
• Stereo Images
• Corner Points
• Planar Regions
• Sum Of Absolute Differences
• Occupancy Grid
• Plane Fitting
• Camera Module
• Scene Geometry
• External Frame
• Sub-pixel
• Surface Appearance