  This work proposes a novel SLAM framework for stereo and visual inertial
odometry estimation. It builds an efficient and robust parametrization of
co-planar points and lines which leverages specific geometric constraints to
improve camera pose optimization in terms of both efficiency and accuracy.
%reduce the size of the Hessian matrix in the optimization. The pipeline
consists of extracting 2D points and lines, predicting planar regions and
filtering the outliers via RANSAC. Our parametrization scheme then represents
co-planar points and lines as their 2D image coordinates and parameters of
planes. We demonstrate the effectiveness of the proposed method by comparing it
to traditional parametrizations in a novel Monte-Carlo simulation set. Further,
the whole stereo SLAM and VIO system is compared with state-of-the-art methods
on the public real-world dataset EuRoC. Our method shows better results in
terms of accuracy and efficiency than the state-of-the-art. The code is
released at https://github.com/LiXin97/Co-Planar-Parametrization.
