  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.
