Autonomous robotic tasks require actively perceiving the environment to achieve application-specific goals. In this paper, we address the problem of positioning an RGB camera to collect the most informative images to represent an unknown scene, given a limited measurement budget. We propose a novel mapless planning framework to iteratively plan the next best camera view based on collected image measurements. A key aspect of our approach is a new technique for uncertainty estimation in image-based neural rendering, which guides measurement acquisition at the most uncertain view among view candidates, thus maximising the information value during data collection. By incrementally adding new measurements into our image collection, our approach efficiently explores an unknown scene in a mapless manner. We show that our uncertainty estimation is generalisable and valuable for view planning in unknown scenes. Our planning experiments using synthetic and real-world data verify that our uncertainty-guided approach finds informative images leading to more accurate scene representations when compared against baselines.

翻译：自主机器人任务需要主动感知环境以实现特定应用目标。本文针对在有限测量预算下，如何定位RGB相机以采集最具信息量的图像来表示未知场景的问题，提出了一种新颖的无地图规划框架，该框架基于已采集的图像测量结果迭代规划下一个最佳相机视角。我们方法的关键在于提出了一种用于图像神经渲染的不确定性估计新技术，该技术通过引导测量采集聚焦于候选视角中不确定性最高的区域，从而在数据收集过程中最大化信息价值。通过逐步向图像集合添加新测量结果，我们的方法以无地图方式高效探索未知场景。实验表明，我们的不确定性估计具有泛化性，且对未知场景的视角规划具有重要价值。基于合成数据与真实数据的规划实验验证了：与基线方法相比，我们提出的不确定性引导方法能够采集到更具信息量的图像，从而生成更准确的场景表征。