EDPLVO: Efficient Direct Point-Line Visual Odometry

This paper introduces an efficient direct visual odometry (VO) algorithm using points and lines. Pixels on lines are generally adopted in direct methods. However, the original photometric error is only defined for points. It seems difficult to extend it to lines. In previous works, the collinear constraints for points on lines are either ignored [1] or introduce heavy computational load into the resulting optimization system [2]. This paper extends the photometric error for lines. We prove that the 3D points of the points on a 2D line are determined by the inverse depths of the endpoints of the 2D line, and derive a closed-form solution for this problem. This property can significantly reduce the number of variables to speed up the optimization, and can make the collinear constraint exactly satisfied. Furthermore, we introduce a two-step method to further accelerate the optimization, and prove the convergence of this method. The experimental results show that our algorithm outperforms the state-of-the-art direct VO algorithms.

Authors

• Lipu Zhou
• Guoquan Huang 0003
• Yinian Mao
• Shengze Wang 0002
• Michael Kaess

Keywords

• Three-dimensional displays
• Closed-form solutions
• Automation
• Optimization methods
• Optimization
• Visual odometry
• Convergence
• 3D Point
• 2D Line
• Degrees Of Freedom
• Point Cloud
• Target Image
• Indoor Environments
• Reference Image
• Line Segment
• Front End
• Depth Estimation
• Previous Algorithms
• Camera Pose
• Translation Vector
• Feature-based Methods
• Loop Closure
• Internal Points
• 2D Point
• Homogeneous Coordinates
• Line Matching
• 3D Line
• Visual-inertial Odometry
• Ground Truth Trajectory
• 3D Segmentation