Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. However, it is difficult to evaluate progress in this area due to a lack of standardized testing which is limited due to the need for active robot agency and perfect object ground-truth. To help provide a standard for testing scene understanding systems, we present a new robot vision scene understanding challenge using simulation to enable repeatable experiments with active robot agency. We provide two challenging task types, three difficulty levels, five simulated environments and a new evaluation measure for evaluating 3D cuboid object maps. Our aim is to drive state-of-the-art research in scene understanding through enabling evaluation and comparison of active robotic vision systems.

中文翻译：

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机器人视觉场景理解挑战

能够探索环境并了解其中所有物体的位置和类型对于必须与人类密切交互的室内机器人平台非常重要。然而，由于缺乏标准化测试，因此很难评估该领域的进展，而标准化测试由于需要主动机器人代理和完美的对象地面实况而受到限制。为了帮助提供测试场景理解系统的标准，我们提出了一个新的机器人视觉场景理解挑战，使用模拟来启用主动机器人代理的可重复实验。我们提供了两种具有挑战性的任务类型、三种难度级别、五种模拟环境和一种用于评估 3D 长方体对象地图的新评估措施。