MEVO: Multi-Environment Visual Odometry

The ego motion estimation from an image sequence, commonly known as visual odometry, has been thoroughly studied in recent years. Different solutions have been developed depending on the particular scenario the system interacts in. In highly textured environments point features are abundant and visual odometry approaches focus on complementary steps, such as sparse bundle adjustment or keyframe techniques, to improve the accuracy of the motion estimation. In textureless scenarios, the absence of point features motivates the use of different image features. Lines have proven to be an interesting alternative to points in man-made environments, but very few visual odometry approaches have been developed using these types of features. Moreover, the combination of point and line features has not been considered in the development of real-time visual odometry algorithms. In this paper, we explore the combination of point and line features to robustly compute the six degree of freedom motion transformation between consecutive stereo frames. Additionally, we deal with the problem of line stereo matching, since our approach is based on 3D-2D correspondences to estimate motion. We develop an efficient algorithm to compute the stereo line matching, even in situations where one of the endpoints describing the line segment in the left image is not visible in the right image. Several experiments with synthetic and real image sequences show that a simple but effective combination of point and line features improves the motion estimate compared to approaches using only one type of these features with a slight increase in computational cost.