Air-SSLAM: A Visual Stereo Indoor SLAM for Aerial Quadrotors

In this letter, we introduce a novel method for visual simultaneous localization and mapping (SLAM)—so-called Air-SSLAM—which exploits a stereo camera configuration. In contrast to monocular SLAM, scale definition and 3-D information are issues that can be more easily dealt with in stereo cameras. Air-SSLAM starts from computing keypoints and the correspondent descriptors over the pair of images, using good features-to-track and rotated-binary robust-independent elementary features, respectively. Then a map is created by matching each pair of right and left frames. The long-term map maintenance is continuously performed by analyzing the quality of each matching, as well as by inserting new keypoints into uncharted areas of the environment. Three main contributions can be highlighted in our method: 1) a novel method to match keypoints efficiently; 2) three quality indicators with the aim of speeding up the mapping process; and 3) map maintenance with uniform distribution performed by image zones. By using a drone equipped with a stereo camera, flying indoor, the translational average error with respect to a marked ground truth was computed, demonstrating promising results.