UAV Autonomous Localization using Macro-Features Matching with a CAD Model

Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has significantly advanced in recent years, mainly due to their relevance in a large variety of commercial, industrial, and military applications. However, UAV navigation in GPS-denied environments continues to be a challenging problem that has been tackled in recent research through sensor-based approaches. This paper presents a novel offline, portable, real-time in-door UAV localization technique that relies on macro-feature detection and matching. The proposed system leverages the support of machine learning, traditional computer vision techniques, and pre-existing knowledge of the environment. The main contribution of this work is the real-time creation of a macro-feature description vector from the UAV captured images which are simultaneously matched with an offline pre-existing vector from a Computer-Aided Design (CAD) model. This results in a quick UAV localization within the CAD model. The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation. Final results reveal the algorithm's low computational burden as well as its ease of deployment in GPS-denied environments

An Onsite Calibration Method for MEMS-IMU in Building Mapping Fields

Light detection and ranging (LiDAR) is one of the popular technologies to acquire critical information for building information modelling. To allow an automatic acquirement of building information, the first and most important step of LiDAR technology is to accurately determine the important gesture information that micro electromechanical (MEMS) based inertial measurement unit (IMU) sensors can provide from the moving robot. However, during the practical building mapping, serious errors may happen due to the inappropriate installation of a MEMS-IMU. Through this study, we analyzed the different systematic errors, such as biases, scale errors, and axial installation deviation, that happened during the building mapping, based on a robot equipped with MEMS-IMU. Based on this, an error calibration model was developed. The problems of the deviation between the calibrated and horizontal planes were solved by a new sampling method. For this method, the calibrated plane was rotated twice; the gravity acceleration of the six sides of the MEMS-IMU was also calibrated by the practical values, and the whole calibration process was completed after solving developed model based on the least-squares method. Finally, the building mapping was then calibrated based on the error calibration model, and also the Gmapping algorithm. It was indicated from the experiments that the proposed model is useful for the error calibration, which can increase the prediction accuracy of yaw by 1–2° based on MEMS-IMU; the mapping results are more accurate when compared to the previous methods. The research outcomes can provide a practical basis for the construction of the building information modelling model