Optimal Global Pose Estimation for Consistent Sensor Data Registration

We consider the problem of consistent range data registration in modeling an unknown environment. The problem is expressed as the optimal estimation of pose variables under the maximum likelihood criterion. By treating all the history of robot poses as variables and solving them simultaneously, consistency is enforced. We formulate relative pose constraints from both matched scans and odometry measurements to construct a network of measurements. Then we derive closed-form pose estimates as well as their covariance matrices. Examples of global scan registration using both real and simulated data are presented.

Authors

• Feng Lu 0001
• Evangelos E. Milios

Keywords

• Robot kinematics
• Mobile robots
• Robot sensing systems
• Computer science
• Maximum likelihood estimation
• History
• Uncertainty
• Covariance matrix
• Robotics and automation
• Error correction
• Pose Estimation
• Global Pose
• Simulated Data
• Optimal Estimation
• Relative Pose
• Robot Pose
• Optimization Problem
• Objective Function
• Coordinate System
• Random Error
• Kalman Filter
• Estimation Problem
• Pair Of Nodes
• Mahalanobis Distance
• Observation Error
• Linkage Types
• Compounding
• Range Of Sensors
• Measurement Equation
• Linear Solution
• Pose Error
• Robot Path
• Unknown Covariance
• Robot Trajectory
• Sufficient Overlap
• Movement Step
• Scan Data