The New Combined Method of the Generation of a 3d Dense Map of Evironment based on History of Camera Positions and the Robot's Movements

Abstract

A combined solution is proposed for solving the point-plane variational problem in a closed-form based on fusion visual features and 3d point clouds. An accurate method for reconstructing 3d scene is introduced, and the closed-form solutions of registration task for orthogonal transformation are presented. This method is used to reconstruct a threedimensional model of the environment from a set of images and depth map. The given method is used to match and register point clouds with an arbitrary spatial resolution and an arbitrary scale with respect to each other. The suggested method improves the convergence and accuracy of reconstruction methods for dynamic, bulky scenes.

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

“…There are multiple works describing stereo-camerabased SLAM methods [15,16]. Similar results can be achieved using the LIDAR sensor, or even single cameras [17][18][19]. There are applications of LIDAR sensors in multi-robot environments [20], and recently a learning-based method was applied using a loop-closing algorithm to further improve the covered trajectory [21].…”