芝生には入るな：音声による弱教師あり学習でレーダーから走行可能ルートを推定
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
音声分類器で地形ラベルを推定し、そのラベルをレーダースキャンに付与することで、悪天候でも機能するレーダーによる走行可能領域のセグメンテーションを弱教師あり学習で実現した。
著者: David Williams, Daniele De Martini, Matthew Gadd, Letizia Marchegiani, Paul Newman
分類: cs.RO, cs.CV
原文アブストラクト
Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment. The performance of LiDAR and vision-based perception systems deteriorates significantly if certain environmental factors are present e.g. rain, fog, darkness. Perception systems based on FMCW scanning radar maintain full performance regardless of environmental conditions and with a longer range than alternative sensors. Learning to segment a radar scan based on driveability in a fully supervised manner is not feasible as labelling each radar scan on a bin-by-bin basis is both difficult and time-consuming to do by hand. We therefore weakly supervise the training of the radar-based classifier through an audio-based classifier that is able to predict the terrain type underneath the robot. By combining odometry, GPS and the terrain labels from the audio classifier, we are able to construct a terrain labelled trajectory of the robot in the environment which is then used to label the radar scans. Using a curriculum learning procedure, we then train a radar segmentation network to generalise beyond the initial labelling and to detect all permissible driving routes in the environment.
