オンボードカメラと疎な航空画像を用いた不整地での滑らかな地形走行学習
Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images
車載カメラと上空からの航空画像を融合し、自己教師あり強化学習で不整地を滑らかに走行しつつ衝突を避ける走行方策を学習した。
著者: Travis Manderson, Stefan Wapnick, David Meger, Gregory Dudek
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual inputs. Our approach applies a hybrid model-based and model-free reinforcement learning method that is entirely self-supervised in labeling terrain roughness and collisions using on-board sensors. Notably, we provide both first-person and overhead aerial image inputs to our model. We find that the fusion of these complementary inputs improves planning foresight and makes the model robust to visual obstructions. Our results show the ability to generalize to environments with plentiful vegetation, various types of rock, and sandy trails. During evaluation, our policy attained 90% smooth terrain traversal and reduced the proportion of rough terrain driven over by 6.1 times compared to a model using only first-person imagery.
