A Novel Resection–Intersection Algorithm With Fast Triangulation Applied to Monocular Visual Odometry

Abstract

This paper proposes a new technique for two-view camera pose estimation applied to vehicular monocular visual odometry. The proposed method is based on the resection–intersection concept using Gauss–Newton non-linear regression to minimize projection residuals. Different from other solutions, the proposed method does not suppose fixed positions of points during resection but rather takes into account the adjustment in the 3-D position of points when calculating the new camera pose increment. A simplified fast two-view triangulation method is used to mitigate the cost of intense 3-D point position recalculation. The method is integrated into a monocular visual odometry solution and tested on the KITTI public dataset. The final system does not use loop closing or key frame selection, and RANSAC is replaced by sequential backward selection. The resection–intersection method presents robustness to errors in initial camera pose and rapid convergence rate, reaching the final camera pose in up to three iterations in 65% of the cases. A single thread Python/OpenCV implementation of the monocular visual odometry system on an Intel Core i7 spent around 75 ms per frame, of which one third was used in the resection–intersection step. The achieved results presented better accuracy than all other published monocular odometry works in the KITTI benchmark, in a simple solution using only two consecutive frames for camera pose estimation.