让机器人自主探索并构建带语义信息的环境地图
SeGuE: Semantic Guided Exploration for Mobile Robots
- 基于视角语义特征评分，选择最优探索位置
- 在仿真与实机实验中均验证了高效性
- 适合需要理解环境语义的智能机器人应用
具身人工智能应用的兴起使机器人能够执行需深入理解环境的复杂任务。为实现此类场景下的机器人成功运行，地图需同时包含几何信息与语义信息。本文提出语义探索新问题：移动机器人需自主探索环境，以完整映射其结构及特征的语义外观。我们基于下一最佳视角探索方法，根据该视角可见的语义特征对潜在位姿进行评分。研究了两种不同的视角采样策略，并在仿真与物理实验中验证了框架的有效性。自动创建高质量的语义地图可使机器人更深入理解并交互于环境，推动未来具身AI应用的便捷部署。
原文摘要 · Abstract (English)
The rise of embodied AI applications has enabled robots to perform complex tasks which require a sophisticated understanding of their environment. To enable successful robot operation in such settings, maps must be constructed so that they include semantic information, in addition to geometric information. In this paper, we address the novel problem of semantic exploration, whereby a mobile robot must autonomously explore an environment to fully map both its structure and the semantic appearance of features. We develop a method based on next-best-view exploration, where potential poses are scored based on the semantic features visible from that pose. We explore two alternative methods for sampling potential views and demonstrate the effectiveness of our framework in both simulation and physical experiments. Automatic creation of high-quality semantic maps can enable robots to better understand and interact with their environments and enable future embodied AI applications to be more easily deployed.
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