Precise Positioning Using Model-Based Maps

This paper addresses the coupled tasks of constructing a spatial representation of the environment with a mobile robot using noisy sensors (sonar) and using such a map to determine the robot's position. The map is not meant to represent the actual spatial structure of the environment so much as it is meant to represent the major structural components of what the robot "sees". This can, in turn, be used to construct a model of the physical objects in the environment. One problem with such an approach is that maintaining an absolute coordinate system for the map is difficult without periodically calibrating the robot's position. The authors demonstrate that in a suitable environment it is possible to use sonar data to correct position and orientation estimates on an ongoing basis. This is accomplished by incrementally constructing and updating a model-based description of the acquired data. Given coarse position estimates of the robot's location and orientation, these can be refined to high accuracy using the stored map and a set of sonar readings from a single position. This approach is then generalized to allow global position estimation, where position and orientation estimates may not be available. The authors consider the accuracy of the method based on a single sonar reading and illustrate its region of convergence using empirical data. >

Authors

• Paul MacKenzie
• Gregory Dudek

Keywords

• Robot kinematics
• Robot sensing systems
• Mobile robots
• Sonar detection
• Position measurement
• Working environment noise
• Intelligent sensors
• Intelligent robots
• Floors
• Fixtures
• Model-based Map
• Correct Position
• Position Estimation
• Mobile Robot
• Single Position
• Orientation Estimation
• High-quality
• True Positive
• Kalman Filter
• Major Axis
• Fault-tolerant
• Position Error
• Line Segment
• Target Model
• Pose Estimation
• Error Vector
• Motion Estimation
• Odometry
• Position Of The Robot
• Convergence Of Estimates
• Orientation Error
• Global Localization
• Correction Vector
• Robot Pose
• Environment Map
• Fraction Of Points
• Sigmoid Function
• Collection Of Observations
• Actual Position