what are some other better or equally as good that you've seen?

@@supernova6553 ORB-SLAM2 is much better in my opinion. Now ORB-SLAM3 is out but I wasn't able to check that out. OpenVSLAM is another great example of such systems.

@@yusufdurkaya5890 better in what? ORB2/3 doesnt know to use odometry , occupancymap is not supported, no tools and unstable

Don't remember exactly what was the computational time at the end, but it was under 1 second, which was the real-time limit set (memory management was enabled). You can see at 50:11 that the first part of the map is retrived from long term memmory to working memory to extend the map. After mapping, just before 55:11, we can see the map in working memory at that time, then I clicked on action to show the global map.

"well" it depends. You will increase loop closure detection success if you revisit the same area with the *same* point of view. The point of view is particularly important in forest, because even by moving 1 feet on the side, the visual features may appear very different because of the changing overlap of trees, branches and leaves. If it is a very windy day with a forest with a lot of leaves, it may increase this effect. Another challenge in this dataset was the lighting variation and shadows, only with SuperPoint I was able to detect the closure of the largest loop (50:05). A GPS was not used in this experiment, but it could have help to find loop closures.

It was partly cloudy and a windy day (clouds were moving relatively fast). For visual odometry, done >10 Hz it was not really a problem. It is more the exposure change when switching from shadow to very sunny area (or vice-versa) that could cause odometry issues. For localization, it was indeed a problem, I tried many features and only SuperPoint was able to close the large loop. Look closely at the bottom left at 50:11, we can see the two images used for the loop closure. We can see one has shadows and sunlights (no clouds), while the other is completely in shadow (with clouds). The wind could also make odometry more difficult, as it would make the sunlights/shadows move on the ground when the trees are moving, thus making features used by odometry moving in the scene.

The easiest way is to downlaod the windows binaries with a supported camera, see github.com/introlab/rtabmap/releases

It depends how large is the area to cover. Lidar may be preferred for fast coverage, if you are interested only in geometry.

You can check this paper for a comparison with standard datasets: introlab.3it.usherbrooke.ca/mediawiki-introlab/images/7/7a/Labbe18JFR_preprint.pdf

Only stereo images and IMU from the camera

@@matlabbe The same camera had both sensors? Was it your phone or a different device?

@@pablodavidarandarodriguez163 this was with a Mynteye camera

See the link in the description

@@matlabbe I don" undesand how can I launch Slam...

It depends what do you mean by real-time, here it means that images were processed in online fashion. I was seeing this on the laptop while walking. For the equipments used, the MYNT EYE S camera linked in the description and a XPS2019 laptop.

You can get windows binaries here: github.com/introlab/rtabmap/releases/tag/0.20.3. Check at 40:00 in the video for parameters used. The only difference is that you won't have access to SuperPoint features for loop closure detection with that release, but other default features should be ok if there is not too much illumination variance.

Fixed!

I recorded it next to where I live.