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.

体现的计算机视觉考虑了在新颖，非结构化环境中对机器人的感知。尤其重要的是体现出来的视觉探索问题：配备摄像头的机器人如何在新的环境中定位？尽管迄今为止取得了进展，但与该问题相关的许多基本问题仍未得到解答：（i）代理商充分探索其环境意味着什么？（ii）哪些方法行得通，在哪些假设和环境条件下有效？（iii）当前的方法在哪些方面不可行，未来的工作可能在哪些方面寻求改进？为了找到这些问题的答案，我们首先介绍现有视觉探索算法的分类法，并创建一个基准测试它们的标准框架。然后，我们使用建议的框架和两个真实感逼真的3D环境，最新的探索架构和各种评估指标，对这四个最新范例进行了全面的实证研究。我们的实验结果提供了见识，并为视觉探索的未来工作提出了新的性能指标和基准。代码，模型和数据是公开可用的。