Robot Navigation Using Modified SLAM Procedure Based on Depth Image Reconstruction

In modern mobile robots, technologies are used to build the most optimal path for its movement. This uses simultaneous navigation and display techniques known as SLAM. A problem with all depth mapping methods is the presence of lost areas. This problem occurs due to poor lighting, mirrored surfaces of objects, or the fine-grained surface of materials, making it impossible to measure depth information. As a result, the effect of overlapping objects appears. It is impossible to distinguish one object from another, or an increase in the object's boundaries (obstacles) occurs. This problem can be solved using image reconstruction techniques. This article presents an approach based on a modified algorithm for finding similar blocks using a neural network. The proposed algorithm also uses the concept of a sparse representation of quaternions, which uses a new gradient to compute the priority function by integrating the quaternion structure with a saliency map. Compared to current technologies, the proposed algorithm provides a plausible reconstruction of the depth map from multimodal images, making it a promising tool for navigation in robot applications. Analysis of the processing results shows that the proposed method allows you to correctly restore the boundaries of objects in the image and depth map, which is a prerequisite for improving the accuracy of the robot's navigation.