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I expect this tech to make big waves in the backend of robotic systems soon. Accurate SLAM with accurate semantic tagging of objects is a big deal. An efficient
by aDyslecticCrow 2y ago
I expect this tech to make big waves in the backend of robotic systems soon. Accurate SLAM with accurate semantic tagging of objects is a big deal. An efficient and accurate reality-to-simulator translation, allowing reinforcement learning in simulated environments to be directly applicable to reality.
Begone lidar units for basic robot tasks! All praise, normal cameras! (though, its far to slow to run on autonomous cars, since the environment changes so rapidly)
- porphyra 2y agoAccurate SLAM with accurate semantic tagging would be a big deal, yes, but this project still relies on pretrained data with COLMAP so how is that relevant to your comment?
- aDyslecticCrow 2y agoI'm not saying you can use THIS method for a SLAM; I'm saying you can use this METHOD for a slam. You don't need a perfect COLMAP for this method (well, not this in particular, but for this method more broadly with some modifications); you just need an approximate location for a few of the images to start and then match the others progressively... which is literally what SLAM is all about. And "pre-trained data" makes no sense. It's trained, as in slowly chewing iteratively on the data before getting decent 3D space, but that just means it's a bit slow. Hence, my mention of simple robots that move in a semi-fixed environment rather than being unusable for self-driving. But more broadly, it's a method to describe the real-world appearance 3d space, which may have computational and flexibility advantages over massive point clouds.