The question of “representation" is central in the context of dense simultaneous localization and mapping (SLAM). Learning-based approaches have the potential to leverage data or task performance to directly inform the representation. However, blending representation learning approaches with “classical" SLAM systems has remained an open question, because of their highly modular and complex nature. A SLAM system 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 over the outputs of this function to learn representations that optimize task performance. However, this is infeasible as several components of a typical dense SLAM system are non-differentiable. In this work, we propose ∇SLAM (gradSLAM), a methodology for posing SLAM systems as differentiable computational graphs, which unifies gradient-based learning and SLAM. We propose differentiable trust-region optimizers, surface measurement and fusion schemes, and raycasting, without sacrificing accuracy. This amalgamation of dense SLAM with computational graphs enables us to backprop all the way from 3D maps to 2D pixels, opening up new possibilities in gradient-based learning for SLAM.
Paper Link: https://ras.papercept.net/proceedings/ICRA20/2444.pdf