# An Improved Two-Step Strategy for Accurate Feature Extraction in Weak-Texture Environments

## TL;DR

This paper introduces a two-step feature extraction method to improve robot perception in environments with little texture.

## Contribution

A novel two-step strategy combining laser-assisted marking and binocular vision for accurate feature extraction in weak-texture environments.

## Key findings

- The method achieves spatial modeling accuracy of ±0.5 mm and a relative error of 2‰.
- The effective feature extraction rate exceeds 97% at a working distance of 1 m.
- The solution supports precise robot tasks like positioning and object grasping in dynamic environments.

## Abstract

To address the challenge of feature extraction and reconstruction in weak-texture environments, and to provide data support for environmental perception in mobile robots operating in such environments, a Feature Extraction and Reconstruction in Weak-Texture Environments solution is proposed. The solution enhances environmental features through laser-assisted marking and employs a two-step feature extraction strategy in conjunction with binocular vision. First, an improved SURF algorithm for feature point fast localization method (FLM) based on multi-constraints is proposed to quickly locate the initial positions of feature points. Then, the robust correction method (RCM) for feature points based on light strip grayscale consistency is proposed to calibrate and obtain the precise positions of the feature points. Finally, a sparse 3D (three-dimensional) point cloud is generated through feature matching and reconstruction. At a working distance of 1 m, the spatial modeling achieves an accuracy of ±0.5 mm, a relative error of 2‰, and an effective extraction rate exceeding 97%. While ensuring both efficiency and accuracy, the solution demonstrates strong robustness against interference. It effectively supports robots in performing tasks such as precise positioning, object grasping, and posture adjustment in dynamic, weak-texture environments.