Selecting Targets for Local Reference Frames

Addresses the problem of seeking out parts of the environment that provide adequate features in order to perform robot localization. The objective is to choose good regions in which local metric maps can be established. A distinctiveness measure is defined as a measure of how well the environment allows the robot to accomplish a task, in our case the task being localization. The distinctiveness measure is evaluated as a function of both the localization strategy and the environment. Areas in the environment are considered to have high distinctiveness measures if they exhibit both sufficient spatial structure and good sensor feedback. The problem is treated as defining an evaluation criterion based on the usefulness of gathered information.

Authors

• Saul Simhon
• Gregory Dudek

Keywords

• Large-scale systems
• Robot kinematics
• Extraterrestrial measurements
• Sonar measurements
• Data mining
• Robot localization
• Feedback
• Mobile robots
• Error correction
• Robot sensing systems
• Local Frame
• Local Reference Frame
• Local Map
• Part Of Environment
• Distinct Measures
• Nonexpansive Mapping
• Quality Measures
• Visual System
• Feature Space
• Physical Measures
• Line Segment
• Position Estimation
• Pose Estimation
• Orthogonal Directions
• Decay Constant
• Localization Techniques
• Coordinate Frame
• Weak Features
• Description Of Environment
• Reference Vector
• Correction Vector
• Dead Reckoning
• Orthogonality Constraint