Accurate and robust UAV navigation in complex urban environments remains challenging due to dense buildings, dynamic obstacles, and unreliable GPS signals. To address this issue, this paper proposes GLP-VO, a hybrid visual odometry framework that combines geometric structure features with point features. An adaptive weighting strategy is introduced to balance the contributions of different feature types according to matching quality and scene complexity, while geometric constraints are incorporated into the optimization process to improve pose estimation accuracy and stability. Experiments on the TUM RGB-D dataset and real UAV flight sequences verify the effectiveness of the proposed method. GLP-VO achieves the best ATE results in five of the ten evaluated TUM sequences, including 0.91 cm on f1_xyz and 0.62 cm on f3_str_tex_far, and remains competitive on challenging sequences such as f2_360_kidnap with an ATE of 2.26 cm. In the ablation study, the full model reduces ATE and RPE by up to 44.9% and 43.1%, respectively. Moreover, the proposed system runs at approximately 35 FPS on the desktop platform and 11 FPS on the onboard platform, demonstrating a favorable balance between accuracy, robustness, and real-time performance.