主动神经映射

摘要

我们解决了使用持续学习的神经场景表示进行主动映射的问题，即主动神经映射。关键在于积极地找到要探索的目标空间，通过高效的 agent 移动，在之前未见过的环境中实时最小化地图不确定性。在本文中，我们研究了持续学习的神经场的权重空间，并通过实证表明，神经变异性，即对随机权重扰动的预测稳健性，可以直接用来衡量神经地图的即时不确定性。结合神经地图中继承的连续几何信息，agent 可以被引导找到可穿越的路径，逐渐获取环境知识。我们首次提出了一种基于坐标的隐式神经表示的在线场景重建的主动映射系统。在视觉逼真的 Gibson 和 Matterport3D 环境中的实验证明了所提出方法的有效性。

English

We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping. The key lies in actively finding the target space to be explored with efficient agent movement, thus minimizing the map uncertainty on-the-fly within a previously unseen environment. In this paper, we examine the weight space of the continually-learned neural field, and show empirically that the neural variability, the prediction robustness against random weight perturbation, can be directly utilized to measure the instant uncertainty of the neural map. Together with the continuous geometric information inherited in the neural map, the agent can be guided to find a traversable path to gradually gain knowledge of the environment. We present for the first time an active mapping system with a coordinate-based implicit neural representation for online scene reconstruction. Experiments in the visually-realistic Gibson and Matterport3D environment demonstrate the efficacy of the proposed method.