Multimodal image inpainting for an autonomous robot navigation application

Automatic 3-D recovery from multimodal images can be extremely useful for information extraction for the robot navigation application. In most cases, such a scene contains missing holes on depth maps that appear during the synthesis from multi-views. This paper presents an automated pipeline for processing multimodal images to 3-D digital surface models. The proposed approach uses the modified exemplar-based technique in quaternion space. We also perform depth completion by fusing data from multiple recorded multimodal images affected by occlusions. We propose an algorithm using the concepts of a sparse representation of quaternions, which uses a new gradient to calculate the priority function by integrating the structure of quaternions with local polynomial approximation - the intersection of confidence intervals). Moreover, the color information incorporates into the optimization criteria to obtain sharp inpainting results. Compared with state-of-the-art techniques, the proposed algorithm provides plausible restoration of the depth map from multimodal images, making them a promising tool for an autonomous robot navigation application.