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Combining 2D to 2D and 3D to 2D Point Correspondences for Stereo Visual Odometry

Self-localization and motion estimation are requisite skills for autonomous robots. They enable the robot to navigate autonomously without relying on external positioning systems. The autonomous navigation can be achieved by making use of a stereo camera on board the robot. In this work a stereo visual odometry algorithm is developed which uses FAST features in combination with the Rotated-BRIEF descriptor and an approach for feature tracking. For motion estimation we utilize 3D to 2D point correspondences as well as 2D to 2D point correspondences. First we estimate an initial relative pose by decomposing the essential matrix. After that we refine the initial motion estimate by solving an optimization problem that minimizes the reprojection error as well as a cost function based on the epipolar constraint. The second cost function enables us to take also advantage of useful information from 2D to 2D point correspondences. Finally, we evaluate the implemented algorithm on the well known KITTI and EuRoC datasets.