LAUSR

Abstract Feature point extraction and matching work together and play a crucial role in many machine vision tasks, such as visual simultaneous localization and mapping (SLAM) which is the basis of robotics. However, feature matching has not been fully studied as much as feature extraction. The most widely used feature matching method is still the classical brute-force matching. In this paper, by fusing the data of inertial measurement unit (IMU), a novel feature points matching algorithm with online learning fault detection, where the global matcher and local matcher operate alternately and switch automatically, is proposed to improve matching accuracy. Taking the uncertainties of the initial states of camera and IMU into account in the initialization phase, the system starts with the global mode to accomplish the matching task and initialize the state variables which are used to align the data of two sensors. Then, once completing the initialization, the algorithm will automatically switch to our new-designed local matcher with feature compensation, which bounds the search space and improves the matching accuracy via performing on the support region according to the results of IMU pre-integration rather than the whole image. In addition, for the purpose of effectively dealing with the misestimation of support region induced by IMU failures, an online learning fault detector based on support vector machines is newly developed, which can trigger the action of mode switcher and runs simultaneously in another thread. Furthermore, a timer is attached to the fault detector in order to activate the detection periodically, which can further reduces the cost of computing resources. Finally, the performance of the present algorithm is validated on public datasets.