论文标题
关于将有效的神经映射应用于无人接地车的实时室内定位
On the Application of Efficient Neural Mapping to Real-Time Indoor Localisation for Unmanned Ground Vehicles
论文作者
论文摘要
来自视觉数据的全球本地化是一个充满挑战的问题，适用于许多机器人域。先前的工作表明，可以训练神经网络以将环境的图像映射到在该环境中的绝对相机姿势，从而在过程中学习隐式神经映射。在这项工作中，我们评估了这种方法在现实世界机器人方案中的适用性，这表明，通过将问题限制为2维并显着增加训练数据的数量，可以使用嵌入式平台实时推断的紧凑型模型来实现几种厘米的本地化精度。我们在UGV平台上部署了训练有素的模型，在Waypoint导航任务中证明了其有效性，其中它能够以9厘米的平均准确度以6fps的速度定位于UGV Onboard CPU上的6FPS，在嵌入式GPU上的35FPS，或在桌面GPU上的220fps。除这项工作外，我们还将发布一个新的本地化数据集，其中包括模拟和真实的环境，每个数据集都有数以万计的训练样本编号。
Global localisation from visual data is a challenging problem applicable to many robotics domains. Prior works have shown that neural networks can be trained to map images of an environment to absolute camera pose within that environment, learning an implicit neural mapping in the process. In this work we evaluate the applicability of such an approach to real-world robotics scenarios, demonstrating that by constraining the problem to 2-dimensions and significantly increasing the quantity of training data, a compact model capable of real-time inference on embedded platforms can be used to achieve localisation accuracy of several centimetres. We deploy our trained model onboard a UGV platform, demonstrating its effectiveness in a waypoint navigation task, wherein it is able to localise with a mean accuracy of 9cm at a rate of 6fps running on the UGV onboard CPU, 35fps on an embedded GPU, or 220fps on a desktop GPU. Along with this work we will release a novel localisation dataset comprising simulated and real environments, each with training samples numbering in the tens of thousands.
