  In this paper we propose an edge-direct visual odometry algorithm that
efficiently utilizes edge pixels to find the relative pose that minimizes the
photometric error between images. Prior work on exploiting edge pixels instead
treats edges as features and employ various techniques to match edge lines or
pixels, which adds unnecessary complexity. Direct methods typically operate on
all pixel intensities, which proves to be highly redundant. In contrast our
method builds on direct visual odometry methods naturally with minimal added
computation. It is not only more efficient than direct dense methods since we
iterate with a fraction of the pixels, but also more accurate. We achieve high
accuracy and efficiency by extracting edges from only one image, and utilize
robust Gauss-Newton to minimize the photometric error of these edge pixels.
This simultaneously finds the edge pixels in the reference image, as well as
the relative camera pose that minimizes the photometric error. We test various
edge detectors, including learned edges, and determine that the optimal edge
detector for this method is the Canny edge detection algorithm using automatic
thresholding. We highlight key differences between our edge direct method and
direct dense methods, in particular how higher levels of image pyramids can
lead to significant aliasing effects and result in incorrect solution
convergence. We show experimentally that reducing the photometric error of edge
pixels also reduces the photometric error of all pixels, and we show through an
ablation study the increase in accuracy obtained by optimizing edge pixels
only. We evaluate our method on the RGB-D TUM benchmark on which we achieve
state-of-the-art performance.
