提出长程路径规划方法，让机器人更高效地重建未知室内环境。
NextBestPath: Efficient 3D Mapping of Unseen Environments
- 不只看眼前视角，而是预测长期目标以避免困在局部区域。
- 在多个数据集上实现更高表面覆盖率，优于当前最优方法。
- 适用于复杂室内场景的高效地图构建，适合智能机器人导航研究者。
本文针对主动3D建图问题，即代理需规划高效轨迹以完整重建新场景。以往方法多聚焦于代理当前位置附近的下一最佳视角，易陷入局部区域。此外，现有室内数据集因几何复杂度有限且真值网格不准确而不足。为此，我们引入新数据集AiMDoom，基于Doom游戏地图生成器，可更好评估多样室内环境中的主动3D建图性能。同时，提出新方法Next-Best-Path（NBP），不再局限于短视视角，而是预测长期目标。该模型联合预测长期目标的累积表面覆盖增益与障碍物地图，实现统一模型下的最优路径规划。通过在线数据收集、数据增强和课程学习，NBP在原有MP3D数据集及新提出的AiMDoom数据集上均显著超越当前最先进方法，在不同复杂度的室内环境中实现更高效的建图。
原文摘要 · Abstract (English)
This work addresses the problem of active 3D mapping, where an agent must find an efficient trajectory to exhaustively reconstruct a new scene. Previous approaches mainly predict the next best view near the agent's location, which is prone to getting stuck in local areas. Additionally, existing indoor datasets are insufficient due to limited geometric complexity and inaccurate ground truth meshes. To overcome these limitations, we introduce a novel dataset AiMDoom with a map generator for the Doom video game, enabling to better benchmark active 3D mapping in diverse indoor environments. Moreover, we propose a new method we call next-best-path (NBP), which predicts long-term goals rather than focusing solely on short-sighted views. The model jointly predicts accumulated surface coverage gains for long-term goals and obstacle maps, allowing it to efficiently plan optimal paths with a unified model. By leveraging online data collection, data augmentation and curriculum learning, NBP significantly outperforms state-of-the-art methods on both the existing MP3D dataset and our AiMDoom dataset, achieving more efficient mapping in indoor environments of varying complexity.
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