RVN-Bench: A Benchmark for Reactive Visual Navigation
AI 摘要
提出了RVN-Bench,一个面向室内移动机器人安全视觉导航的碰撞感知基准。
主要贡献
- 提出了一个新的碰撞感知视觉导航基准RVN-Bench
- 提供了大规模、多样化的室内环境,基于Habitat 2.0和HM3D
- 定义了碰撞感知的导航任务和评估指标
方法论
构建于Habitat 2.0模拟器,利用HM3D场景,提供在线强化学习环境、轨迹图像数据集生成器和负轨迹数据集。
原文摘要
Safe visual navigation is critical for indoor mobile robots operating in cluttered environments. Existing benchmarks, however, often neglect collisions or are designed for outdoor scenarios, making them unsuitable for indoor visual navigation. To address this limitation, we introduce the reactive visual navigation benchmark (RVN-Bench), a collision-aware benchmark for indoor mobile robots. In RVN-Bench, an agent must reach sequential goal positions in previously unseen environments using only visual observations and no prior map, while avoiding collisions. Built on the Habitat 2.0 simulator and leveraging high-fidelity HM3D scenes, RVN-Bench provides large-scale, diverse indoor environments, defines a collision-aware navigation task and evaluation metrics, and offers tools for standardized training and benchmarking. RVN-Bench supports both online and offline learning by offering an environment for online reinforcement learning, a trajectory image dataset generator, and tools for producing negative trajectory image datasets that capture collision events. Experiments show that policies trained on RVN-Bench generalize effectively to unseen environments, demonstrating its value as a standardized benchmark for safe and robust visual navigation. Code and additional materials are available at: https://rvn-bench.github.io/.