机器人2024，Vol.46Issue(3)：284-293,304,11.DOI:10.13973/j.cnki.robot.230253
一种室内弱纹理环境下的视觉SLAM算法
A Visual SLAM Algorithm in Indoor Weak Texture Environment
蔡显奇 1王晓松 1李玮1
作者信息
- 1. 中国计量大学机电工程学院,浙江杭州 310018
- 折叠
摘要
Abstract
A visual-inertial SLAM(simultaneous localization and mapping)optimization algorithm is proposed to address the problem that visual SLAM is not robust and accurate for indoor robots in weak texture environment.Firstly,the deep learning module based on the attention mechanism is used to directly match features between two adjacent frames,and then the traditional feature detection methods are adopted to solve the problem that enough feature points can't be extracted.Secondly,a camera depth confidence model is established to reduce the drift error of long-distance features by introducing the depth confidence probability of the spatial points before calculating the inter-frame pose transformation of the camera.Finally,the deep confidence of all matched feature points involved in back-end optimization is used as the piecewise threshold of dynamic robust kernel function to optimize the traditional bundle adjustment and coordinate the overall motion trajectory.It is shown in real scene experiments that the proposed algorithm is significantly robust in weak texture environments.Compared with VINS-RGBD algorithm,the absolute trajectory error of the proposed algorithm is reduced by 50.38％and the relative trajectory error is reduced by 85.75％.关键词
视觉SLAM/弱纹理环境/RGB-D相机/特征置信/鲁棒核函数Key words
visual SLAM(simultaneous localization and mapping)/weak texture environment/RGB-D camera/feature confidence/robust kernel function引用本文复制引用
蔡显奇,王晓松,李玮..一种室内弱纹理环境下的视觉SLAM算法[J].机器人,2024,46(3):284-293,304,11.
