Artificial Intelligence and Machine Vision

ORB-SLAM - Uses   - Bundle Adjustment   - ORB features [9]   - A pose graph      - Essential graph      - a spanning tree        - loop closure links        - strong edges        - from covisibility graph   - covisibliity graph     - local covisible area     - tracking and mapping - mar point and keyframe selection   - generous spawning   - restrictive culling   - identify redundant keyframes   - improves robustness and lifelong operations - Stores map points:   - 3D position X(w,i) in the world coordinate system   - the viewing direction n(i)     - the mean unit vector of all its viewing directions     - the ray that joint the point with the optical center of the keyframe  - A representative ORB descriptor D(i)     - the associated ORB descriptor whose hamming distance is minimum       with respect to all other associated descriptors in the keyframes       in which the point is observed.  - the maximum d(max)  - the minimum d(min) distance     - a

TUM RGB-D benchmark [38] - an excellent dataset to evaluate the accuracy of camera location - several sequences with accurate ground truth obtained with an external motion capture system KITTI - extracted 2000 corners - 512x384 - 752x480 - 1241x376 - 5 corners per cell Compute orientation & ORB descriptors  - novel, direct, semi-dense, LSD-SLAM [10]     - takes time to converge the depth values - PTAM benchmarks [4]   - manually selected two keyframes for initialization - align the keyframe trajectories using the similarity transformatione - scale is unknown - measure the absolute trajectory error (ATE) [38] - RGB-D SLAM [43]    - trajectories - use the similarity transform to check if the scale is well recovered. - align the trajectories with a rigid body transformation