Aiming at the problems of low accuracy and easy to fall into local optimal solutions of the existing lens distortion parameter estimation methods, a catadioptric omnidirectional camera lens distortion parameter method based on the improved Aquila Optimization (AO) algorithm is proposed. Firstly, the optimization ability of the AO algorithm is enhanced by integrating chaotic mapping, adaptive adjustment strategy, and population optimization strategy, which solves the problems of slow convergence speed and proneness to falling into local optimal solutions. Secondly, the distribution range of distortion parameters is derived and determined by the corresponding distortion edges of straight lines in the space and the single parameter division model. Then, the optimization objective function containing the distortion parameters is constructed. Finally, the improved AO algorithm is used to find the best distortion parameters for the optimization objective function. After analyzing the correction results of standard gallery images and omnidirectional images, the method proposed in this paper estimates the main point error within 0.5 pixels and the radial aberration coefficient error within 2.5%. It can effectively estimate the lens aberration parameters and realize the omnidirectional image aberration correction. It improves the visual navigation system's image quality under the task of environment perception and is valuable in engineering applications.