用语义图谱提升机器人导航能力，精准定位且抗干扰。
Semantic Environment Atlas for Object-Goal Navigation
- 构建包含场景与物体关系的语义图谱，以稀疏节点记录视觉地标。
- 在Habitat任务中成功率达39.0%，较现有方法提升12.4%。
- 适合需要高精度定位与鲁棒导航的智能体应用。
本文提出语义环境图谱（Semantic Environment Atlas, SEA），一种新型映射方法，旨在增强具身智能体的视觉导航能力。SEA利用语义图地图，精细刻画场所与物体之间的关系，丰富导航上下文。该地图基于图像观测构建，将视觉地标作为稀疏编码节点存储于环境中。SEA可整合多个环境的语义地图，保留场所-物体关系的记忆，对视觉定位与导航任务具有重要价值。我们开发了基于SEA的导航框架，并在视觉定位与物标导航任务中进行评估。基于SEA的定位框架显著优于现有方法，能从单张查询图像准确识别位置。在Habitat场景的实验表明，该方法成功率达39.0%，比当前最先进方法提高12.4%，同时在存在噪声里程计与执行误差条件下仍保持鲁棒性，且计算开销低。
原文摘要 · Abstract (English)
In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships between places and objects, thereby enriching the navigational context. These maps are constructed from image observations and capture visual landmarks as sparsely encoded nodes within the environment. The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships, which proves invaluable for tasks such as visual localization and navigation. We developed navigation frameworks that effectively leverage the SEA, and we evaluated these frameworks through visual localization and object-goal navigation tasks. Our SEA-based localization framework significantly outperforms existing methods, accurately identifying locations from single query images. Experimental results in Habitat scenarios show that our method not only achieves a success rate of 39.0%, an improvement of 12.4% over the current state-of-the-art, but also maintains robustness under noisy odometry and actuation conditions, all while keeping computational costs low.
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