基于改进Census变换的单目视觉里程计

针对直接法视觉里程计在光照变化场景下的失效问题，提出基于改进Census变换的单目视觉里程计，向量Census变换半直接单目视觉里程计（VC-SVO）. Census变换是立体视觉领域中非参数变换的一种，可以有效减少光照变化对图像的影响. 将Census变换引入SLAM中的后端优化，改变传统Census变换的形式，转换到欧氏空间中表示，并采用新的误差计算方法. 在SVO算法中增添非平面假设模型，扩展SVO算法并融合改进后的Census变换，通过最小化地图点的Census变换误差来得到更准确的相机位姿，同时构建环境地图. 在EuRoC、New Tsukuba Stereo与TUM公开数据集上的图像实验表明，VC-SVO实现了光照变化情况下的位姿估计，验证了算法的有效性. VC-SVO算法的精度和鲁棒性要优于已开源的SVO和基于直接法的大范围定位和地图构建（LSD-SLAM）算法.

Monocular visual odometry based on improved Census transform

A monocular visual odometry based on improved Census transform, vector-Census semi-direct monocular visual odometry (VC-SVO), was proposed, in order to solve the failure problem of the direct visual odometry in the scene of illumination changes. Census transform is a kind of non-parametric transformation in the field of stereo vision, which can effectively reduce the impact of illumination change on the image. The Census transform was introduced into the back-end optimization in SLAM, the expression of traditional Census transform was improved, and the improved Census transform was transformed into Euclidean space successfully. At the same time, a new method to measure the degree of difference was designed. The non-plane hypothesis model of the scene was added into SVO algorithm. The SVO algorithm was extended and the improved Census transform was integrated. The Census transform was integrated to obtain more accurate camera pose by minimizing the Census transform error of the map points, at the same time, the environment map was constructed. Image experiment results on EuRoC, New Tsukuba Stereo and TUM dataset demonstrate the effectiveness and accuracy of the algorithm in illumination change situation. The accuracy and robustness were better than that of the open source SVO and large-scale direct monocular simultaneous localization and mapping (LSD-SLAM) algorithm.