Multimodal image inpainting for an autonomous robot navigation application

N. Gapon; V. Voronin; E. Semenishchev; Y. Ilyukhin; D. Bezuglov; A. Zelenskii

doi:10.1117/12.2594476

20 June 2021 Multimodal image inpainting for an autonomous robot navigation application

N. Gapon, V. Voronin, E. Semenishchev, Y. Ilyukhin, D. Bezuglov, A. Zelenskii

Proceedings Volume 11785, Multimodal Sensing and Artificial Intelligence: Technologies and Applications II; 117850Y (2021) https://doi.org/10.1117/12.2594476
Event: SPIE Optical Metrology, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

N. Gapon, V. Voronin, E. Semenishchev, Y. Ilyukhin, D. Bezuglov, and A. Zelenskii "Multimodal image inpainting for an autonomous robot navigation application", Proc. SPIE 11785, Multimodal Sensing and Artificial Intelligence: Technologies and Applications II, 117850Y (20 June 2021); https://doi.org/10.1117/12.2594476

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