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。