Embodied computer vision considers perception for robots in novel,
unstructured environments. Of particular importance is the embodied visual
exploration problem: how might a robot equipped with a camera scope out a new
environment? Despite the progress thus far, many basic questions pertinent to
this problem remain unanswered: (i) What does it mean for an agent to explore
its environment well? (ii) Which methods work well, and under which assumptions
and environmental settings? (iii) Where do current approaches fall short, and
where might future work seek to improve? Seeking answers to these questions, we
first present a taxonomy for existing visual exploration algorithms and create
a standard framework for benchmarking them. We then perform a thorough
empirical study of the four state-of-the-art paradigms using the proposed
framework with two photorealistic simulated 3D environments, a state-of-the-art
exploration architecture, and diverse evaluation metrics. Our experimental
results offer insights and suggest new performance metrics and baselines for
future work in visual exploration. Code, models and data are publicly
available: this https URL

本研究探讨了具有摄像机配备的机器人在新颖，非结构化环境中的感知问题，特别是如何进行视觉探索问题，提出了现有视觉探索算法的分类法并创建了一个标准框架进行基准测试。在两个超现实的 3D 环境和多种评估指标下，通过对四种状态下最先进的模式进行彻底的实证研究，为视觉探索领域提供了新的性能指标和基准。