不整地走行性を多目的に学習する手法
Learning multiobjective rough terrain traversability
高解像度地形データと車両シミュレーションを用い、速度・エネルギー・加速度の3指標で走行性を予測する深層学習モデルを提案し、未見の森林地形で90%の精度を達成した。
著者: Erik Wallin, Viktor Wiberg, Folke Vesterlund, Johan Holmgren, Henrik Persson, Martin Servin
分類: cs.RO, cs.CE, cs.LG
原文アブストラクト
We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed, energy consumption, and acceleration. The measures are continuous and reflect different objectives for planning that go beyond binary classification. A deep neural network is trained to predict the traversability measures from the local heightmap and target speed. To produce training data, we use an articulated vehicle with wheeled bogie suspensions and procedurally generated terrains. We evaluate the model on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90%. Predictions rely on features from the high-dimensional terrain data that surpass local roughness and slope relative to the heading. Correlations show that the three traversability measures are complementary to each other. With an inference speed 3000 times faster than the ground truth simulation and trivially parallelizable, the model is well suited for traversability analysis and optimal path planning over large areas.
