Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry
This work addresses a specific bottleneck in SLAM systems for robotics and autonomous navigation, offering incremental improvements in optimization performance.
The paper tackles the problem of improving camera pose optimization in stereo SLAM and visual-inertial odometry by proposing a novel parametrization of co-planar points and lines, which demonstrates better accuracy and efficiency than state-of-the-art methods on the EuRoC dataset.
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.