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.

我们提出了一种具有不断学习的神经场景表示的主动映射问题，即主动神经映射。关键在于通过高效的代理移动主动地找到要探索的目标空间，从而在以前未见过的环境中实时地最小化地图的不确定性。本文研究了不断学习的神经场中的权重空间，并通过实验证明了神经变异性，即针对随机权重扰动的预测稳健性，可以直接用于测量神经映射的即时不确定性。结合神经映射中继承的连续几何信息，代理可以被引导找到可穿越的路径，逐渐了解环境。我们首次提出了一种具有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 环境中进行的实验证明了该方法的有效性。