论文标题
接下来在哪里探索？授予历史意识的自主3D探索
Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration
论文作者
论文摘要
在这项工作中，我们解决了使用深度摄像头对未知室内环境进行自动3D探索的问题。我们将问题视为对下一个最佳视图（NBV）的估计，从而最大程度地提高了未知区域的覆盖范围。我们通过将NBV估计作为分类问题进行重新构建来做到这一点，并提出了一种基于学习的新型度量，该指标既编码当前的3D观察（深度框架）和正在进行的重建历史。这项工作的主要贡献之一是为3D重建历史记录引入新的表示形式，作为辅助实用图像，该图与当前的深度观察有效相结合。通过这两种信息，我们训练一个名为Ouncistcnn的轻巧CNN，该CNN估计NBV是深度传感器找到最尚未探索的区域的一组方向。我们对合成房间和真实房间扫描进行了广泛的评估，表明拟议中的授权能够使用3D环境的完整知识来处理甲骨文的勘探性能。
In this work we address the problem of autonomous 3D exploration of an unknown indoor environment using a depth camera. We cast the problem as the estimation of the Next Best View (NBV) that maximises the coverage of the unknown area. We do this by re-formulating NBV estimation as a classification problem and we propose a novel learning-based metric that encodes both, the current 3D observation (a depth frame) and the history of the ongoing reconstruction. One of the major contributions of this work is about introducing a new representation for the 3D reconstruction history as an auxiliary utility map which is efficiently coupled with the current depth observation. With both pieces of information, we train a light-weight CNN, named ExHistCNN, that estimates the NBV as a set of directions towards which the depth sensor finds most unexplored areas. We perform extensive evaluation on both synthetic and real room scans demonstrating that the proposed ExHistCNN is able to approach the exploration performance of an oracle using the complete knowledge of the 3D environment.
