论文标题
立体声和视觉惯用探针仪的共平面参数化
Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry
论文作者
论文摘要
这项工作提出了一个新颖的SLAM框架，用于立体声和视觉惯性探测器估计。它建立了共同平面点和线条的有效且可靠的参数化，该参数利用了特定的几何约束，以根据效率和准确性来改善相机姿势优化。 ％在优化中降低了Hessian矩阵的大小。该管道包括提取2D点和线，预测平面区域并通过RANSAC过滤异常值。然后，我们的参数化方案表示共面点和线作为平面的2D图像坐标和参数。我们通过将其与新型蒙特卡洛模拟集中的传统参数化进行比较来证明该方法的有效性。此外，将整个立体声猛击和VIO系统与公共现实世界中数据集Euroc的最新方法进行了比较。我们的方法在准确性和效率方面比最先进的方法显示出更好的结果。该代码在https://github.com/lixin97/co-planar-parametrization上发布。
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.
