AlejandroSilvestri

@mslavescu , the only one thing I can say is that LSD-SLAM managed to run on a mobile phone - only in localization mode. I suppose that means LSD-SLAM in...

@mslavescu , impressive. Yes, that the Android demo I was talking about. I believe with reinitialization you mean "relocalization" in ORB-SLAM jargon. ORB-SLAM uses BoW and features to relocalize (and...

Well, I think ORB-SLAM doesn't exactly to your needs. It doesn't register a dense cloud, on the contrary, it make an extra effort to maintain as less point as possible....

@KarimHabbab92, FAST is deterministic, with the same parameters on the same image the resulting keypoints will be the same. ORB-SLAM2 use a quadtree (with the odd name ComputeKeyPointsOctTree) to limit...

@KarimHabbab92 , I believe the main problem is in descriptor matching. Descriptors are meant to uniquely identify a keypoint by its surrounding visual appearance. ORB-SLAM2 uses BRIEF descriptors (because ORB...

@KarimHabbab92 If you are interest, look for these papers: - FAST: features from accelerated segment test - BRIEF: Binary Robust Independent - ORB: an efficient alternative to SIFT or SURF...

@didnotwork Monocular can't get scale. That's final. No one asks monocular slam to get scale, because it's impossible: the information is not there. Monocular slam can get structure up to...

@didnotwork Scaling the initial map is not enough, because monocular slam suffers scale drifting. The system needs a scale tip periodically at least. IMU readings do this. Loop closure too,...

@didnotwork Stereo has the scale, not the direction. Usually you must to translate and rotate the trajectory to fit ground truth.

@didnotwork I believe this can help: [A tutorial on quantitative trajectory evaluation for visual intertial odometry](https://www.researchgate.net/publication/330586253_A_Tutorial_on_Quantitative_Trajectory_Evaluation_for_Visual-Inertial_Odometry) As you can see, the alignment problem is so complex that a paper addressing...