ReVNM：基于远程摄像头的学习型视觉导航
ReVNM: Learning-Based Visual Navigation from a Remote Camera
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中文总结 AI 辅助
本文提出ReVNM，利用单个远程监控摄像头作为观察源和隐式地图，通过exo2ego模块预测自我中心深度，实现无需预建地图的视觉导航，并在仿真和真实环境中验证了有效性。
中文摘要 AI 辅助
视觉导航模型（VNMs）使机器人能够仅依靠自我中心的视觉观察进行导航，而无需几何定位和规划，但长距离导航仍然需要预先构建的地图。本文提出了远程视觉导航模型（ReVNM），该模型利用单个远程监控摄像头同时作为观察来源和隐式环境地图，用于视觉导航。虽然使用远程摄像头可以消除对预构建地图以及机载视觉处理的需求，但其有限的视野而非自我中心的观察使得实现无碰撞导航变得困难。缺乏具有多样化远程视角的现有数据（这些数据对于训练稳健的VNMs至关重要）进一步加剧了这一挑战。在这项工作中，我们提出了一种基于合成学习的方法来应对这一双重挑战。我们的ReVNM扩展了最先进的VNM架构，增加了一个外部视角到自我中心视角（exo2ego）模块，该模块从远程摄像头观察中预测自我中心的深度观察。这有助于VNM在考虑机器人前方障碍物的同时规划路径。仅在具有多样化障碍物布局和摄像头视角的随机生成世界中进行训练，ReVNM无需额外微调即可很好地泛化到真实机器人导航。在仿真和真实环境中的实验均证实了所提出方法的有效性。
英文摘要
Visual Navigation Models (VNMs) enable robots to navigate from egocentric visual observations without geometric localization and planning, but long-range navigation still requires pre-built maps. This paper presents the Remote Visual Navigation Model (ReVNM), which uses a single remote surveillance camera to serve as both an observation source and an implicit environmental map for visual navigation. While the use of remote cameras could eliminate the need for pre-built maps as well as onboard vision processing, their limited field of view instead of egocentric observations makes it hard to achieve collision-free navigation. The lack of existing data with diverse remote viewpoints, which are crucial for training robust VNMs, further complicates the challenge. In this work, we propose a learning-by-synthesis approach to address this two-fold challenge. Our ReVNM extends a state-of-the-art VNM architecture with an exocentric-to-egocentric (exo2ego) module that predicts an egocentric depth observation from remote-camera observations. This helps the VNM to plan a path while considering obstacles in front of the robot. Trained only on randomly generated worlds with diverse obstacle layouts and camera viewpoints, ReVNM can generalize well to real robot navigation without additional fine-tuning. Experiments in both simulation and real-world environments confirmed the effectiveness of the proposed approach.
发表机构
- University of Tsukuba（筑波大学）
- CyberAgent AI Lab
- Nagoya University（名古屋大学）
机构由 AI 辅助整理，请以论文原文为准。
