A Multi-Feature Fusion Slam System Attaching Semantic In-Variant to Points and Lines

Research on solving heading attitude of airdrop cargo platform based on line features

The present study envisages the development of an improved line features method to accurately estimate the attitude of the airdrop cargo platform during airdrop landing. Therefore, this article uses the geometric characteristics of the line features to improve the traditional line features extraction and removes the locally dense line features in the image, which greatly reduces the number of line features in the image. Then, the improved random sample consensus is used to remove the mismatching of line features, which improves the real-time performance of the algorithm and the accuracy of the attitude angle, and makes up for the problem of difficult extraction of point features or low matching accuracy in the airdrop environment. Finally, a constraint equation is established for the line features that are successfully matched, and using homography to obtain attitude of the airdrop cargo platform. This article also meets the requirements of accurate calculation attitude of airdrop cargo platform. The experiment shows the significance and feasibility of the airdrop cargo platform heading and attitude calculation technology based on the line feature, and it has a good application prospect.