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.

翻译：在同步定位与建图中，主动闭环是一项主动视觉问题，旨在通过视觉引导机器人最大化重访已探索位置的概率，从而重置行进过程中增量构建地图所累积的漂移误差。然而，当前主流的导航策略将此类不完整地图作为工作空间先验知识加以利用，在现代长期自主长距离行进场景中往往失效，因为此时地图累积误差变得显著。为克服基于地图的导航方法的这些局限性，本文首次在具身人工智能领域探索无地图导航，特别是利用目标物体导航技术（通常缩写为ON、ObjNav或OGN），该技术可在不使用先验地图的情况下高效探索目标物体。具体而言，本研究从现成的无地图ON规划器出发，将其扩展为可利用先验地图，并进一步证明通过最小化“ALC损失”与“ON损失”，可使长距离ALC的性能达到最优。本研究提出了一种名为ALC-ON的简洁高效方法，通过融合前沿引导、数据驱动及大语言模型引导的ON技术，推动具有挑战性的长距离ALC技术的发展。