  Visual odometry is important for plenty of applications such as autonomous
vehicles, and robot navigation. It is challenging to conduct visual odometry in
textureless scenes or environments with sudden illumination changes where
popular feature-based methods or direct methods cannot work well. To address
this challenge, some edge-based methods have been proposed, but they usually
struggle between the efficiency and accuracy. In this work, we propose a novel
visual odometry approach called \textit{EdgeVO}, which is accurate, efficient,
and robust. By efficiently selecting a small set of edges with certain
strategies, we significantly improve the computational efficiency without
sacrificing the accuracy. Compared to existing edge-based method, our method
can significantly reduce the computational complexity while maintaining similar
accuracy or even achieving better accuracy. This is attributed to that our
method removes useless or noisy edges. Experimental results on the TUM datasets
indicate that EdgeVO significantly outperforms other methods in terms of
efficiency, accuracy and robustness.
