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RGBD Point Cloud Alignment Using Lucas-Kanade Data Association and Automatic Error Metric Selection
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AI
DOI：10.1109/TRO.2015.2489479.png)
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Abstract
En 中文
We propose to overcome a significant limitation of the iterative closest point (ICP) algorithm used by KinectFusion, namely, its sole reliance upon geometric information. Our approach uses both geometric and color information in a direct manner that uses all the data in order to accurately estimate camera pose. Data association is performed by Lucas-Kanade to compute an affine warp between the color images associated with two RGBD point clouds. A subsequent step then estimates the Euclidean transformation between the point clouds using either a point-to-point or point-to-plane error metric, with a novel method based on a normal covariance test for automatically selecting between them. Together, Lucas-Kanade data association with covariance testing enables robust camera tracking through areas of low geometric features, without sacrificing accuracy in environments in which the existing ICP technique succeeds. Experimental results on several publicly available datasets demonstrate the improved performance both qualitatively and quantitatively.
Keywords:
Camera tracking
iterative closest point (ICP)
Kinect
KinectFusion
Lucas-Kanade
mapping