Multi-source fusion SLAM algorithm based on improved point-Line features
Traditional visual SLAM algorithms are difficult to detect and track features in scenes lacking obvious features or when mobile platforms move rapidly.This article proposes a multi-source fusion SLAM algorithm based on improved point line fea-tures.The front-end improves the EDLines line feature extraction algorithm by combining similar short line segments,extracting long line segment features.The back-end integrates point,line features,and IMU data,and uses nonlinear optimization methods to further estimate the camera pose.The experimental results show that the improved EDLines line feature extraction algorithm has a feature extraction speed that is four times faster than the traditional LSD line feature extraction algorithm.In the testing of the EuRoc dataset,this algorithm has good positioning and image building effects in different scenarios,and has high robustness and real-time performance,which has great reference value for real-time obstacle avoidance navigation applications of robots.
SLAM algorithmEDLines algorithmIMUpoint line featuresnonlinear optimization
NETL
NSTL
万方数据
维普
