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gradSLAM: Automagically differentiable SLAM
[article]
2020
arXiv
pre-print
Blending representation learning approaches with simultaneous localization and mapping (SLAM) systems is an open question, because of their highly modular and complex nature. Functionally, SLAM is an operation that transforms raw sensor inputs into a distribution over the state(s) of the robot and the environment. If this transformation (SLAM) were expressible as a differentiable function, we could leverage task-based error signals to learn representations that optimize task performance.
arXiv:1910.10672v3
fatcat:y6aoujprevbs3k72lve6bmttqa