Vision-based loop closing for delayed state robot mapping

This paper shows results on outdoor vision-based loop closing for simultaneous localization and mapping. Our experiments show that for loops of over 50 m, the pose estimates maintained with a delayed-state extended information filter are consistent enough to guarantee assertion of vision- based pose constraints for loop closure, provided no necessary information links are added to the estimator. The technique computes relative pose constraints via a robust least squares minimization of 3D point correspondences, which are in turn obtained from the matching of SIFT features over candidate image pairs. We propose a loop closure test that checks both for closeness of means and for highly informative updates at the same time.

Authors

• Viorela Ila
• Juan Andrade-Cetto
• Rafael Valencia
• Alberto Sanfeliu

Keywords

• Robot vision systems
• Simultaneous localization and mapping
• Testing
• Information filters
• Information filtering
• Delay estimation
• Robustness
• Performance evaluation
• Computer vision
• Feature extraction
• Loop Closure
• Robot Mapping
• Image Pairs
• Feature Matching
• Pose Estimation
• Relative Pose
• White Noise
• Visual Features
• Point Cloud
• Kalman Filter
• Mahalanobis Distance
• Mapping Process
• Fisher Information
• Extended Kalman Filter
• Odometry
• Vehicle Motion
• Current Pose
• Robot Pose
• Bhattacharyya Distance
• Markov Blanket