Unmanned Aerial Vehicles (UAV) have been heavily studied in the past decade, where autonomous flights have been a popular subject. More complex applications have led to higher requirements on the autonomous flight algorithms and the absence of performance data complicates the selection of what algorithm to use for various applications. Therefore, this thesis focused in analyzing the performance difference between two methods, Simultaneous Localization AndMapping (SLAM) and Artificial Potential Field Approach (APFA), which are planning and reactive algorithms, respectively. Fundamental dynamics were applied, Feedback Linear Controllers (FBLC)s for stabilization and an odometry position model combined with an inverse dynamics technique that linearizes the non-linear odometry model. The SLAM approach was set up in four steps: landmark extraction which uses a point distance based method for segment separation, combined with a Split-And-Merge algorithm for extracting linear landmarks, data association that validates the landmarks, Extended Kalman Filter (EKF) that uses the landmarks together with the odometry model for estimating the position of the UAV, and a modified TangentBug as the reactive algorithm. The APFA was constructed of two functions, an attractive and a repulsive function. The two methods were implemented on the robotics simulation platform Virtual Robot Experimentation Platform (V-REP), where a quadcopter was used as the model for the UAV. All theory was implemented onto the quadcopter model and embedded scripts were used for communication within V-REP, mainly through internal Application Programming Interface (API)-functions. Furthermore, a script was written that randomly generates three different types of simulation environments. The implementation of both methods was analyzed in reaching an arbitrary goal position in terms of: the most successful, the most time efficient and the safest navigation path. Another thing analyzed was the time- and space-complexity of both implemented methods. The results stated that the implemented APFA and the SLAM approach had approximately equal success rate, SLAM had the safest navigation, was the most time efficient, and had the highest time- and space-complexity for a worst case scenario. One of the conclusions were that improvements could be done in the implementations. Future work includes adding a proper damping method, improving the flaws in the implemented methods as well as to use V-REP as a Robot Operating System (ROS)-node for creating a Software In The Loop (SITL)-simulation, in order to achieve more realistic simulations.