Recent literature in real-time trajectory planning has proposed using Control Barrier Functions (CBFs) as collision constraints in Model Predictive Control (MPC) for efficient guidance, a concept referred to as MPC-CBF. This concept has been explored for both first and second-order CBFs. However, these approaches relied on an analytical description of the environment. Building upon this, we propose combining MPC-CBF with Euclidean Signed Distance Fields (ESDFs), eliminating the need for such an analytical model of the environment. Notably, we extend this approach to a new field by applying it to Unmanned Aerial Vehicles (UAVs). Through simulations, we compare flown trajectories and noise robustness for distance constraints, first-order CBF constraints and second-order CBF constraints. First-order CBF constraints outperform distance constraints, excelling in path planning and noise resilience. Second-order CBF constraints face challenges due to numerical approximations of the hessian of the ESDF and stricter dependency on an accurate acceleration model, limiting their practicality for UAVs. The proposed control framework was tested by safely maneuvering an enterprise inspection drone around a Boeing 787-9 aircraft inside an aircraft hangar, confirming its effectiveness in collision avoidance and real-world scenarios.