  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.
