论文标题
固执：室内对象导航的强大基线
Stubborn: A Strong Baseline for Indoor Object Navigation
论文作者
论文摘要
我们提出了一个强大的基线，该基线超过了先前发表的关于在室内环境中导航到目标对象的栖息地挑战任务的方法。我们的方法是从先前最新的主要故障模式中激发的：由于不精确的地图构造，探索不良，对象识别不准确和代理被困。我们为减轻这些问题做出了三项贡献：（i）首先，我们表明现有的基于地图的方法无法有效地使用语义线索进行探索。我们提出了一种语义不足的探索策略（称为顽固），而没有任何令人惊讶的学习能力超过先前工作的知识。 （ii）我们提出了一种整合时间信息以改善对象识别的策略。 （iii）最后，由于深度观察的不准确，该试剂经常被困在小区域中。我们为障碍物识别开发了一个多尺度碰撞图，以减轻此问题。
We present a strong baseline that surpasses the performance of previously published methods on the Habitat Challenge task of navigating to a target object in indoor environments. Our method is motivated from primary failure modes of prior state-of-the-art: poor exploration, inaccurate object identification, and agent getting trapped due to imprecise map construction. We make three contributions to mitigate these issues: (i) First, we show that existing map-based methods fail to effectively use semantic clues for exploration. We present a semantic-agnostic exploration strategy (called Stubborn) without any learning that surprisingly outperforms prior work. (ii) We propose a strategy for integrating temporal information to improve object identification. (iii) Lastly, due to inaccurate depth observation the agent often gets trapped in small regions. We develop a multi-scale collision map for obstacle identification that mitigates this issue.
