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.
翻译:机器人本地化是使用地图和传感器测量找到机器人外形的反向问题。 近年来, 不可逆的神经网络( INNs) 成功解决了各个领域的模糊反向问题。 本文提出了一个解决INN本地化问题的框架。 我们设计了一个INN, 向前方路径提供隐含的地图代表, 向反向路径提供本地化。 通过在评估中取样潜在空间, 本地化输出的机器人以千差万别的方式生成, 可用于估计不确定性。 我们显示本地化N( IN) 的本地化性能与当前低潜值的方法相同。 我们展示了本地化N 的详细 2D 和 3D 地图重建, 利用本地化向培训集外端显示外观。 我们还提供了一种使用本地化算法处理绑架问题的全球本地化算法 。