Visual-inertia simultaneous localization and mapping based on point-and-line features
The accuracy of mobile robot localization in low-texture scenes is often compromised,leading to frequent tracking loss.To solve this problem,this study proposes an innovative point-line feature extraction and matching strategy incorporated into the visual-inertial simultaneous localization and mapping(SLAM)system.The approach begins by proposing a line feature extraction and matching algorithm.Refining the hidden parameters of the line feature extraction algorithm improves accuracy.Subsequently,diverse matching screening frameworks for point-line features are employed to reduce mismatches.This approach results in a line feature extraction matching algorithm suitable for the visual-inertial SLAM system.By integrating the proposed line feature constraint into the current vis-ual-inertial framework,this study establishes a robust visual-inertial SLAM system suitable for operation in unknown low-texture environments.Experimental validation with a mobile robot in a real-world setting demonstrates superior accuracy and robustness of the proposed strategy compared with those of the existing visual-inertial framework.The system enhances indoor localization accuracy by 24.2%and corridor localization accuracy by 8%,providing sub-stantial value for high-precision mobile robot localization in low-texture scenes.
国家科技期刊平台
NSTL
万方数据
维普
