视觉里程计
人工智能
计算机科学
稳健性(进化)
同时定位和映射
初始化
计算机视觉
特征(语言学)
里程计
像素
概率逻辑
模式识别(心理学)
移动机器人
机器人
生物化学
化学
语言学
哲学
程序设计语言
基因
作者
Shing Yan Loo,Ali Amiri,Syamsiah Mashohor,Sai Hong Tang,Hong Zhang
标识
DOI:10.1109/icra.2019.8794425
摘要
Reliable feature correspondence between frames is a critical step in visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) algorithms. In comparison with existing VO and V-SLAM algorithms, semi-direct visual odometry (SVO) has two main advantages that lead to state-of-the-art frame rate camera motion estimation: direct pixel correspondence and efficient implementation of probabilistic mapping method. This paper improves the SVO mapping by initializing the mean and the variance of the depth at a feature location according to the depth prediction from a single-image depth prediction network. By significantly reducing the depth uncertainty of the initialized map point (i.e., small variance centred about the depth prediction), the benefits are twofold: reliable feature correspondence between views and fast convergence to the true depth in order to create new map points. We evaluate our method with two outdoor datasets: KITTI dataset and Oxford Robotcar dataset. The experimental results indicate that improved SVO mapping results in increased robustness and camera tracking accuracy. The implementation of this work is available at https: //github.com/yan99033/CNN-SVO
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