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.

本文使用Canny-VO作为RGBD视觉里程计系统，并将它应用于自由曲线注册的经典问题。针对边缘注册中常用距离变换的两种替代方法提出了Approximate Nearest Neighbour Fields和Oriented Nearest Neighbour Fields。为了保证系统在异常值和噪声存在的情况下的鲁棒性，注册问题被形式化为最大后验概率问题，并通过迭代重新加权最小二乘法来解决。最后在公共SLAM基准序列上进行了广泛评估，证明了该系统具有最先进的性能和优于欧几里德距离场的特点。

基于几何 3D-2D 边缘对齐的 RGB-D 摄像头视觉里程计