Vision-based mapping with backward correction

We consider the problem of creating a consistent alignment of multiple 3D submaps containing distinctive visual landmarks in an unmodified environment. An efficient map alignment algorithm based on landmark specificity is proposed to align submaps. This is followed by a global minimization using the close-the-loop constraint. Landmark uncertainty is taken into account in the pairwise alignment and the global minimization process. Experiments show that the pairwise alignment of submaps with backward correction produces a consistent global 3D map. Our vision-based mapping approach using sparse 3D data is different from other existing approaches which use dense 2D range data from laser or sonar rangefinders.

Authors

• Stephen Se
• David G. Lowe
• James J. Little

Keywords

• Uncertainty
• Simultaneous localization and mapping
• Intelligent robots
• Airports
• Sonar
• Mobile robots
• Constraint optimization
• Intelligent systems
• Intelligent networks
• Iris
• Global Minimum
• Global Map
• Pairwise Alignment
• Map Alignment
• Covariance Matrix
• Local Image
• 3D Coordinates
• Constrained Optimization
• Alignment Results
• Mobile Robot
• Landmark Localization
• Odometry
• Number Of Squares
• Scale-invariant Feature Transform
• Part Of The Map
• Least-squares Minimization
• Bundle Adjustment
• Local Image Features
• Long-term Drift
• 3D Landmarks