Attitude Detection and Localization for Unmanned Aerial Vehicles

Design and implementation of DATA logging and stabilization system for a UAV

“…The accelerometer makes this quite easy, as just a few short calculations are required. All of these procedures have to be performed after each iteration's readout of the sensors' results [ 24 ].…”

Information Fusion of GPS, INS and Odometer Sensors for Improving Localization Accuracy of Mobile Robots in Indoor and Outdoor Applications

“…26,30,[34][35][36][37][38][39][40][41][42][43] Many research studies have reported GPS-INS and GPS-odometer integration schemes utilizing KFs for effective localization and positioning. 14,23,25 It is observed that the KFbased fusion proves effective in reducing the error accumulation in positioning due to INS and odometer at an acceptable level. 27 The Artificial Intelligence-based sensor data fusion methods using multilayer ANNs are reported in ref.…”

“…Similarly, to accomplish its mission, an unmanned aerial vehicle (UAV) aircraft must know its location, speed and heading angle with high precision and accuracy along with its starting as well as target location. 13,14 For localization, sensing in mobile robots is achieved using various exteroceptive and proprioceptive sensors 13 which include inertial sensors (i.e., orthogonal accelerometers and gyroscopes), odometer, the global positioning system (GPS) and visual sensors. 15 For instance, in UAVs, the visual sensing mechanism is used for localization which includes monocular, stereo and fish-eye cameras.…”

“…No sensor is perfect and therefore each sensor has some limitations to its performance, for instance, in micro-electro-mechanical systems (MEMS), the inertial sensors, accelerometers and gyroscopes are used to measure linear accelerations and orientation (angular) rates in three-dimensional space. 14,18 The nonlinearities and uncertainties associated with the sensor measurements due to bias instabilities and the stochastic noise factors degrade the performance of the localization system to a greater extent. 19 The main source of error in MEMS inertial sensors is the bias uncertainty due to which the computed attitudes, positions and velocities 20 from the IMU suffer from high levels of noise and drifts.…”