Local_INN: 可逆ニューラルネットワークによる暗黙的な地図表現と自己位置推定
Local_INN: Implicit Map Representation and Localization with Invertible Neural Networks
可逆ニューラルネットワーク(INN)を使い、順方向で暗黙的な地図表現、逆方向で自己位置推定を同時に行う枠組みを提案。低遅延で従来手法と同等の精度を実現し、グローバル自己位置推定も可能にした。
著者: Zirui Zang, Hongrui Zheng, Johannes Betz, Rahul Mangharam
分類: cs.RO
原文アブストラクト
Robot localization is an inverse problem of finding a robot's pose using a map and sensor measurements. In recent years, Invertible Neural Networks (INNs) have successfully solved ambiguous inverse problems in various fields. This paper proposes a framework that solves the localization problem with INN. We design an INN that provides implicit map representation in the forward path and localization in the inverse path. By sampling the latent space in evaluation, Local\_INN outputs robot poses with covariance, which can be used to estimate the uncertainty. We show that the localization performance of Local\_INN is on par with current methods with much lower latency. We show detailed 2D and 3D map reconstruction from Local\_INN using poses exterior to the training set. We also provide a global localization algorithm using Local\_INN to tackle the kidnapping problem.
