Towards Robust Localization : Deep Feature Extraction with Convolutional Neural Networks

Abstract: The ability for autonomous robotics to localize themselves in the environment is crucial and tracking the change of features in the environment is key for visual based odometry and localization. When shifting into rough environments of dust, smoke and poor illumination as well as erratic movements common in MAVs however, that task becomes substantially more difficult. This thesis explores the ability of the deep classifier CNN architecture to retain detailed and noise tolerant feature maps out of sensor fused images for feature tracking in the context of localization. The proposed method is enriching the RGB image with data from thermal images which is fed into a AlexNet or VGG-16 and extracted as a feature map at a specific layer. This feature map is used to detect feature points and is used to pair feature points between frames resulting in a discrete vector field of feature change. Preliminary complementary methods for the selection of channels are also developed.