Local Consistent Alignment for 3D modeling with an RGB-D camera

3D model building is an essential topic in both computer vision and robotic community. Usually this is achieved with alignment methods which try to place the data into a common reference frame by finding out the transformations between them. Traditional local alignment approaches, such as ICP and its variants, mainly concern about pairwise constraints and leave out the transformation consistency requirements implied by data sequence. However, these consistencies are important clues to increase the precision of alignment. To use this information better, we present Local Transformation Consistent Alignment (LTCA), which takes triple of frames as input and simultaneously optimize the transformations between them so that local consistencies can be maintained. To evaluate the proposed method, we apply it to the 3D modeling problem with RGB-D image data as input. Comparisons with the state-of-the-art pairwise alignment- Generalized ICP are also made. Experimental results show that more precise alignment results can be obtained when consistency constraints are kept.