DocumentCode :
3709597
Title :
Entropy based keyframe selection for Multi-Camera Visual SLAM
Author :
Arun Das;Steven L. Waslander
Author_Institution :
Mechanical and Mechatronics Engineering, University of Waterloo, Canada
fYear :
2015
Firstpage :
3676
Lastpage :
3681
Abstract :
Although many state-of-the-art visual SLAM algorithms use keyframes to help alleviate the computational requirements of performing online bundle adjustment, little consideration is taken for specific keyframe selection. In this work, we propose two entropy based methods which aim to insert keyframes that will directly improve the system´s ability to localize. The first approach inserts keyframes based on the cumulative point entropy reduction in the existing map, while the second approach uses the predicted point flow discrepancy to select keyframes which best initializes new features for the camera to track against in the future. We implement the proposed methods within the Multi-Camera Parallel Mapping and Tracking framework, and demonstrate the effectiveness of our methods using ground truth data collected using an indoor positioning system.
Keywords :
"Entropy","Cameras","Jacobian matrices","Visualization","Uncertainty","Mathematical model","Covariance matrices"
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on
Type :
conf
DOI :
10.1109/IROS.2015.7353891
Filename :
7353891
Link To Document :
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