Active Neural Mapping
This addresses the problem of efficient online scene reconstruction for robotics or autonomous systems, though it appears incremental as it builds on existing neural representations.
The paper tackles active mapping in unseen environments by using a continually-learned neural scene representation to guide agent movement and minimize map uncertainty, demonstrating efficacy in Gibson and Matterport3D environments.
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.