FPEVO: Fused point-edge visual odometry for low-structured and low-textured scenes

Abstract

Visual odometry is an essential component of vision-based robotic navigation systems. A primary limitation of existing visual odometry solutions is their inability to achieve satisfactory performance in both high- and low-textured regions. In this paper, a robust RGB-D visual odometry method is proposed that fuses point and edge features. By combining the descriptiveness of feature points with the structure provided by edge data, a method that is robust to low-textured scenes is developed. Edge features are first detected and grouped based on the Gestalt principles of continuity and proximity. Edge groups are then associated between the current and previous frames using point features in the vicinity of the edges. Pose estimation is thereafter performed by first matching points between associated edge groups, filtering these points based on structural constraints imposed by the edges, and estimating the motion of the agent. Compared to state-of-the-art alternatives, such as REVO, MSC-VO, DROID-VO and SplaTAM on the TUM RGB-D, ICL-NUIM and Tartan-Air datasets, the resulting method reduces the root mean square absolute trajectory error, and translational and rotational relative pose errors by up to 58%, 75%, and 82%, respectively. This indicates that our method is not only more accurate than current approaches, but also more consistent, especially in low-structured and low-textured environments.