Replacing Projective Data Association with Lucas-Kanade for KinectFusion

We propose to overcome a significant limitation of the KinectFusion algorithm, namely, its sole reliance upon geometric information to estimate camera pose. Our approach uses both geometric and color information in a direct manner that uses all the data in order to perform the association of data between two RGBD point clouds. Data association is performed by aligning the two color images associated with the two point clouds by estimating a projective warp using the Lucas-Kanade algorithm. This projective warp is then used to create a correspondence map between the two point clouds, which is then used as the data association for a point-to-plane error minimization. This approach to correspondence allows camera tracking to be maintained through areas of low geometric features. We show that our proposed LKDA data association technique enables accurate scene reconstruction in environments in which low geometric texture causes the existing approach to fail, while at the same time demonstrating that the new technique does not adversely affect results in environments in which the existing technique succeeds.