Application of machine vision image feature recognition in 3D map construction

Abstract

Simultaneous localization and mapping algorithm is a technique to simultaneously construct a map of the environment by the sensors it carries. In this paper, we focus on the SLAM (Simultaneous Localization and Mapping) algorithm based on machine vision-depth information, overlap region determination among local sub-maps, and fusion algorithm of 3D dense maps, and analyze four modules of front-end visual odometry, back-end optimization, loopback detection, and map construction. The front-end visual odometry adopts the ORB (Oriented FAST and Rotated BRIEF) algorithm based on the feature point method to extract and match the features between image frames and uses the iterative nearest point method to solve the camera motion between adjacent images. Meanwhile, a 3D feature descriptor based on the histogram of spatial distribution is designed to encode the feature information around the key points, and the Kd-Tree algorithm is used for radial nearest neighbor search to complete the feature matching. Finally, the 3D map is fused by the 3D-ICP algorithm. The experiments prove that the low-dimensional local feature descriptor designed has good descriptiveness and is superior, and the 3D map fusion based on this descriptor has a good fusion effect and can meet the accuracy requirements of practical applications.

Keywords

• Machine vision
• Map construction
• Machine learning
• Feature description