Eliminating Scale Drift in Monocular SLAM Using Depth from Defocus

Publication Type:
Journal Article
IEEE Robotics and Automation Letters, 2018, 3 (1), pp. 581 - 587
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© 2017 IEEE. This letter presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene.
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