Multimodal learning has advanced the performance for many vision-language
tasks. However, most existing works in embodied dialog research focus on
navigation and leave the localization task understudied. The few existing
dialog-based localization approaches assume the availability of entire dialog
prior to localizaiton, which is impractical for deployed dialog-based
localization. In this paper, we propose DiaLoc, a new dialog-based localization
framework which aligns with a real human operator behavior. Specifically, we
produce an iterative refinement of location predictions which can visualize
current pose believes after each dialog turn. DiaLoc effectively utilizes the
multimodal data for multi-shot localization, where a fusion encoder fuses
vision and dialog information iteratively. We achieve state-of-the-art results
on embodied dialog-based localization task, in single-shot (+7.08% in
Acc5@valUnseen) and multi- shot settings (+10.85% in Acc5@valUnseen). DiaLoc
narrows the gap between simulation and real-world applications, opening doors
for future research on collaborative localization and navigation.

DiaLoc 是一种新的基于对话的定位框架，通过迭代细化位置预测来可视化每次对话后的当前姿态，有效地利用多模态数据进行分步定位。DiaLoc 在单次拍摄（ValUnseen 准确率 + 7.08%）和多次拍摄（ValUnseen 准确率 + 10.85%）设置中取得了最先进的结果，缩小了模拟与实际应用之间的差距，为未来的协作定位和导航研究打开了新的大门。

DiaLoc：一个迭代式的具身对话定位方法

DiaLoc: An Iterative Approach to Embodied Dialog Localization

We address the challenging task of Localization via Embodied Dialog (LED).
Given a dialog from two agents, an Observer navigating through an unknown
environment and a Locator who is attempting to identify the Observer's
location, the goal is to predict the Observer's final location in a map. We
develop a novel LED-Bert architecture and present an effective pretraining
strategy. We show that a graph-based scene representation is more effective
than the top-down 2D maps used in prior works. Our approach outperforms
previous baselines.

本研究提出了一种基于 LED-Bert 结构的 Localization via Embodied Dialog 方法，使用基于图的场景表示来预测观察者在地图上的最终位置，实验结果表明该方法优于之前的基准方法。