Mr. Vadim Egipko Profile

Vadim Egipko

at Moscow State Univ. of Technology "STANKIN"

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Proceedings Article | 17 May 2022 Poster + Paper

3D reconstruction for SLAM using multisensor fusion and block-based inpainting

A. Zelensky, V. Voronin, N. Gapon, E. Semenishchev, V. Egipko, I. Khamidullin

Proceedings Volume 12138, 121380X (2022) https://doi.org/10.1117/12.2625905

KEYWORDS: Image fusion, Computer programming, Reconstruction algorithms, 3D image processing, Neural networks, Image processing, Visual process modeling, Sensors

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Simultaneous localization and mapping (SLAM) systems are useful for camera tracking, and 3-D reconstructions may be desired for many robotic tasks. There is a problem consisting of a decrease in the accuracy of planning the movement trajectory caused by incorrect sections on the depth map due to incorrect distance determination to objects. Such defects appear as a result of poor lighting, specular or fine-grained surfaces of objects. As a result, the effect of increasing the boundaries of objects (obstacles) appears, and the overlapping of objects makes it impossible to distinguish one object from another. In this paper, we propose a multisensor SLAM system capable of recovering a globally consistent 3-D structure. The proposed method mainly takes two steps. The first step is to fusion images from visible cameras and depth sensors based on the PLIP model (parameterized model of logarithmic image processing) close to the human visual system's perception. The second step is image reconstruction. This article presents an approach based on a modified exemplar block-based algorithm using the autoencoder-learned local image descriptor for image inpainting. For this purpose, we learn the descriptors using a convolutional autoencoder network. Then, a 3-D point cloud is generated by using the reconstructed data. Our system outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on a benchmark for evaluating RGB-D SLAM systems.

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