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is slam possible for a hobbyist or is it only possible inside money loaded universities ?

I think that boils down alot of the other comments.

SLAM is hard. There are alot of 2D packages out there because they're reliable and robust. Most of the time people that have a 2D lidar also have another form of 3D sensing for obstacle avoidance. The lidar is for positioning and longer range obstacle detection than a depth camera or a sonar can handle.

Once you leave the planar-land of SLAM, you are now entering an active research field. Within ROS, its great that anyone can publish a package and have it available for the world, but that also creates varying quality and maintained implementations. This is especially true in edge research fields were every couple years someone's new approach deems their prior work no longer state of the art. None of the packages that I do know of visual or dense SLAM are going to be trivial for a hobbyist to configure or use (needing high quality and highly calibrated cameras / extrinsic). Many times, these are use-case specific and may not be as general as lidar slam. ORB-SLAM is the one I usually point people to, but there are also about 5 different popular implementations with their own special quirks on GitHub.

My recommendation is as a hobbyist unless your interest is SLAM, don't leave planar-land. Use the lidar slam packages and use that D435i you bought as an obstacle avoidance camera to give you 3D sensing. Or maybe add in some visual odometry to improve your positioning.