用于目标导航的语义环境图集

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nuri Kim, Jeongho Park, Mineui Hong, Songhwai Oh
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引用次数: 0

摘要

在本文中,我们介绍了语义环境图集(SEA),这是一种新颖的制图方法,旨在增强具身代理的视觉导航能力。语义环境图集利用语义图地图复杂地勾勒出地点和物体之间的关系,从而丰富了导航环境。这些图是根据图像观测结果构建的,将视觉地标作为环境中的稀疏编码节点进行捕捉。SEA 整合了来自不同环境的多个语义地图,保留了地点与物体之间关系的记忆,这对于视觉定位和导航等任务来说非常宝贵。我们开发了能有效利用 SEA 的导航框架,并通过视觉定位和目标导航任务对这些框架进行了评估。我们基于 SEA 的定位框架明显优于现有方法,能从单个查询图像中准确识别位置。Habitat Savva等人(2019)的实验结果表明,我们的方法不仅实现了39.0%的成功率--比目前最先进的方法提高了12.4%,而且还能在嘈杂的里程测量和致动条件下保持鲁棒性,同时保持较低的计算成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Environment Atlas for Object-Goal Navigation

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 Savva et al. (2019)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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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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