Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection

Panoramic images provide distinct advantages in odometry applications, which are largely due to their extensive field of view and higher information density captured in a single frame. Traditional odometry methods often rely on mapping panoramic images onto the planar structure for feature tracking. However, this process introduces uneven distortions of features, which diminish the accuracy of feature tracking and odometry, particularly in scenarios involving large displacements. In this work, we address this challenge by introducing a novel approach, named spherical congruence projection (SCP), that maps panoramic images onto a spherical structure and projects the spherical pixels onto a two-dimensional data format while preserving the spherical pixel topology. SCP effectively eliminates the distortion across the panoramic image. Additionally, we present the optical flow odometry on the panoramic image in the spherical structure and integrate it with the proposed SCP method for the first time. The experimental results in public and custom-built datasets demonstrate that the proposed SCP-based odometry method reliably tracks features and maintains accurate odometry performance, even in fast-moving scenarios.