论文标题
Canny-VO：基于几何3D-2D边比对的RGB-D摄像机的视觉进程
Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment
论文作者
论文摘要
本文回顾了自由形式曲线登记的经典问题，并将其应用于称为Canny-Vo的有效RGBD视觉探光系统，因为它有效地跟踪了从图像中提取的所有Canny Edge特征。提出了两个在边缘注册中常用的距离转换的替换：大约最近的邻居字段和定向的最近的邻居场。从效率和准确性角度来看，3D2D Edge Arignment从这些替代配方中受益。它消除了对数据型模型登记，双线性插值和次级梯度计算的范式更高的需求。为了确保在存在异常值和传感器噪声的情况下系统的稳健性，将注册设为最大后验问题，并通过迭代重新加权最小二乘法解决了由此产生的加权最小二乘物镜。研究了各种健壮的重量功能，并根据残留误差的统计数据做出最佳选择。效率还通过对最近的邻居场的自适应采样定义提高了效率。对公共SLAM基准序列的广泛评估表明，与经典欧几里得距离领域相比，最先进的性能和优势。
The present paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO, as it efficiently tracks all Canny edge features extracted from the images. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy. It removes the need for the more computationally demanding paradigms of datato-model registration, bilinear interpolation, and sub-gradient computation. To ensure robustness of the system in the presence of outliers and sensor noise, the registration is formulated as a maximum a posteriori problem, and the resulting weighted least squares objective is solved by the iteratively re-weighted least squares method. A variety of robust weight functions are investigated and the optimal choice is made based on the statistics of the residual errors. Efficiency is furthermore boosted by an adaptively sampled definition of the nearest neighbour fields. Extensive evaluations on public SLAM benchmark sequences demonstrate state-of-the-art performance and an advantage over classical Euclidean distance fields.
