基于深度学习特征点法的单目视觉里程计

Monocular visual odometry based on deep learning feature point method

针对特征点法的视觉里程计VO中光度、视点变化对特征点提取稳定性降低的不利影响,提出一种基于深度学习特征点法的单目VO方法。采用自监督深度学习网络训练得到DSP特征点检测器。首先使用亮度非线性逐点调整方法对训练图像进行光度调整;然后使用非极大值抑制方法剔除冗余DSP特征点,改进最邻近方法得到双向最邻近方法,解决特征点匹配问题;最后建立最小化重投影误差方程求解优化位姿及空间点参数。在Hpatches、Visual Odometry数据集上进行验证,实验结果表明:DSP特征点检测器增强了特征匹配对光度、视点变化的鲁棒性;无后端优化的条件下,本方法定位均方根误差比ORB方法明显降低,且保证了系统实时性,为特征点法的VO提供新的解决思路。

Aiming at the adverse effect of luminosity and viewpoint change on feature point extraction stability in Visual Odometry(VO)of feature point method,a monocular VO method based on deep learning feature point method is proposed.The deep learning SuperPoint(DSP)feature point detector is obtained by self-supervised deep learning network training.Firstly,the brightness of the training image is adjusted by the brightness nonlinear point-by-point adjustment method.Secondly,the redundant DSP feature points are eliminated by using the non-maximum value suppression method.The two-way nearest neighbor algorithm is improved based on the nearest neighbor algorithm to solve the feature point matching problem.Finally,the equation for minimizing the reprojection error is established to solve the optimal pose and spatial point parameters.The experimental results on Hpatches and Visual Odometry datasets show that the DSP feature point detector enhances the robustness of feature matching to luminosity and viewpoint changes.Without the backend optimization,this method reduces the root mean square error obviously in comparison to ORB method.The real-time performance of the system is guaranteed,which provides a new solution for the VO of feature-based method.