Study on feature extraction algorithm of mobile robot vision SLAM under dynamic illumination

摘要

When a feature point detection method is used in vision SLAM to match images, environment condition around the robot is uncertain usually. Many influence factors such as rotation, scale, fuzzy as well as illumination in the process of detection have a strong impact on robot's locating and incremental map building. Experiments proved that SIFT, SURF, BRISK, ORB and FREAK have good robustness under normal illumination. However, the illumination is complex in practical applications, and the stability of image features extraction will be affected. Based on a mobile robot vision platform, the speed, repetition rate and matching rate of five feature extraction algorithms above are compared and analyzed with different methods. Under dynamic illumination, the robustness and matching effect of image features with translation, rotation, scale and fuzzy transformations are also compared. Through experimental data analyzing, BRISK features shows better effect under dynamic illumination.