提出无地图导航新方法，提升长距离回环闭合效率
ON as ALC: Active Loop Closing Object Goal Navigation
- 结合目标导航与回环闭合损失，优化机器人路径规划
- 在长距离任务中实现更高回环闭合成功率，优于传统地图依赖方法
- 适合长期自主导航场景，尤其适用于无可靠地图环境
在同时定位与建图中，主动回环闭合（ALC）是一种主动视觉问题，旨在引导机器人最大化重访已访问点的概率，从而重置旅行过程中累积的漂移误差。然而，当前主流导航策略依赖不完整地图作为先验知识，在现代长时程、远距离自主任务中因地图累积误差显著而失效。为此，本文首次在具身人工智能领域探索无地图导航，特别利用无需地图先验的目标导航（ON）技术高效搜寻目标物体。具体而言，本文从一个现成的无地图ON规划器出发，扩展其使用地图能力，并进一步证明：通过最小化‘回环闭合损失’和‘目标导航损失’，可最大化长距离主动回环闭合（LD-ALC）性能。本研究提出一种简单有效的方案ALC-ON（ALCON），通过融合前沿引导、数据驱动及大语言模型引导的ON技术，加速长距离回环闭合技术的发展。
原文摘要 · Abstract (English)
In simultaneous localization and mapping, active loop closing (ALC) is an active vision problem that aims to visually guide a robot to maximize the chances of revisiting previously visited points, thereby resetting the drift errors accumulated in the incrementally built map during travel. However, current mainstream navigation strategies that leverage such incomplete maps as workspace prior knowledge often fail in modern long-term autonomy long-distance travel scenarios where map accumulation errors become significant. To address these limitations of map-based navigation, this paper is the first to explore mapless navigation in the embodied AI field, in particular, to utilize object-goal navigation (commonly abbreviated as ON, ObjNav, or OGN) techniques that efficiently explore target objects without using such a prior map. Specifically, in this work, we start from an off-the-shelf mapless ON planner, extend it to utilize a prior map, and further show that the performance in long-distance ALC (LD-ALC) can be maximized by minimizing ``ALC loss" and ``ON loss". This study highlights a simple and effective approach, called ALC-ON (ALCON), to accelerate the progress of challenging long-distance ALC technology by leveraging the growing frontier-guided, data-driven, and LLM-guided ON technologies.
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