Large-scale Loop-closing with Pictorial Matching

This paper presents a mapping method that can accurately map large environment with one single robot by visiting the environment for only once, and the resulting map can provide thorough 3D description for the environment in a predefined global coordinate. Our first contribution is to represent the map as a collection of submaps arranged in a deformable configuration, and to perform loop-closing by registering this submap configuration to an aerial image. The second contribution is to introduce the active contour technique to the SLAM domain, so that the registration is efficiently solved in an iterative energy minimization process. The constraints from robot mapping are modeled as forces trying to keep the submaps consistent to each other, while the pictorial matching is represented by forces guiding submaps to a globally consistent configuration. In the experiment, we demonstrate the proposed algorithm's capability to close a 1, 890 meters with only one visiting. The result is compared with ground truth, and high accuracy is observed

Authors

• Cheng Chen
• Han Wang 0001

Keywords

• Large-scale systems
• Simultaneous localization and mapping
• Robot kinematics
• Active contours
• Satellites
• Constraint optimization
• Impedance matching
• Iterative algorithms
• Energy capture
• Filtering
• Energy Minimization
• Aerial Images
• Active Contour
• Global Coordinates
• Single Robot
• Energy Function
• Point Cloud
• Global Information
• Unmanned Aerial Vehicles
• Mapping Results
• Graphical Model
• Vector Field
• Coordinate Transformation
• 3D Point
• Fisher Information
• Homogeneous Regions
• Posterior Mode
• Map Information
• Maximum A Posteriori
• Target Probability
• Pictorial Information
• Unmanned Ground Vehicles
• Onboard Sensors
• Vehicle Trajectory
• Energy Minimization Problem
• Image Domain
• Mapping Process
• Global Perspective