主動神經映射

摘要

我們探討了具有持續學習神經場景表示的主動映射問題，即主動神經映射。關鍵在於主動尋找要探索的目標空間，通過高效的代理移動，在之前未見環境中即時最小化地圖不確定性。本文中，我們研究了持續學習神經場的權重空間，並實證表明神經變異性，即對隨機權重擾動的預測穩健性，可以直接用於衡量神經地圖的即時不確定性。結合神經地圖中繼承的連續幾何信息，代理可以被引導找到可通過的路徑，逐漸獲取對環境的認識。我們首次提出了一個基於座標的隱式神經表示的主動映射系統，用於在線場景重建。在視覺逼真的Gibson和Matterport3D環境中的實驗證明了所提方法的有效性。

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.