关键词: 深度估计, 视觉里程计, 尺度, 预处理共轭梯度, 定位

Abstract:

Aiming at the obvious scale uncertainty problem of the direct method DSO （Direct Sparse Odometry）, the impact of scale uncertainty on the system positioning accuracy is analyzed, and using a deep learning network that performs depth prediction on a single image is proposed. For the time-consuming problem of the back-end, a Preconditioned Conjugate Gradient（PCG） algorithm is utilized to accelerate the back-end solution. Compared with ORB-SLAM2, DSO, LDSO on the KITTI public dataset, the accuracy of localization of the system is significantly improved.

Key words: depth estimation, visual odometry, scale, preconditioned conjugate gradient, localization