# 变化物体布局中的终身小物体导航：基准与方法

> 原文链接：https://robotics.hellobike.com/papers/2026-10-07-2610-10125
> 频道：papers | 更新日期：2026-10-07 | 标签：arxiv, cs.RO
> 核心摘要：本文提出LiSoNav-COL任务、LiSoNav-Eval基准和IVAM-Nav方法，并在室内场景小物体导航序列上验证效果。

# 变化物体布局中的终身小物体导航：基准与方法

::: v-pre
> 原文链接：[Lifelong small-object navigation in changing object layouts: a benchmark and method](https://arxiv.org/abs/2610.10125)（arXiv 2610.10125 · 发布日期：2026-10-07）

**Lifelong small-object navigation in changing object layouts: a benchmark and method**

本文提出LiSoNav-COL任务、LiSoNav-Eval基准和IVAM-Nav方法，并在室内场景小物体导航序列上验证效果。

## 核心要点

- 面向家庭机器人在同一环境中持续导航到小型可移动物体，需处理遮挡、观测困难和物体搬移。
- IVAM-Nav通过多视角检查主动观察支撑表面，并将记忆锚定到观测视点以便复用和再验证。
- LiSoNav-Eval覆盖28个室内场景和45类小物体，包含未变化与重定位目标以评估记忆适应。
- 实验显示IVAM-Nav相对代表性方法表现更优；数据集和代码已提供。

**验证范围与局限**：验证范围为LiSoNav-Eval中的室内场景终身导航序列，摘要未说明真机实验。

## 英文摘要 (Abstract)

Household robots need to continually navigate to different objects in the same environment, many of which are small and portable, such as tools and toys. Their small visual footprint and frequent occlusion make reliable observation difficult, and they may be moved by people without the robot observing the changes. We formulate this challenging task as Lifelong Small-object Navigation in Changing Object Layouts (LiSoNav-COL). Agents must seek suitable viewpoints for reliable observation, accumulate and reuse scene knowledge to efficiently locate subsequent targets, and update outdated memory after object relocation. To eliminate the need for prior scene scanning, we also require agents to start navigation with empty scene memory. Although practical, this task still lacks benchmarks designed around its defining assumptions. To bridge this gap, we introduce LiSoNav-Eval, a dedicated benchmark spanning 28 indoor scenes with 45 small-object categories. Its lifelong navigation sequences include both unchanged and relocated targets to evaluate memory reuse and adaptation to object relocation. To address this challenging task, we propose a navigation method based on multi-view Inspection with Viewpoint-Anchored Memory, dubbed IVAM-Nav. IVAM-Nav actively observes supporting surfaces from complementary viewpoints for reliable small-object perception and anchors the resulting memory to their observation viewpoints, supporting relational memory reuse and revalidation under similar viewing conditions. Extensive experiments on LiSoNav-Eval demonstrate favorable performance of IVAM-Nav against representative methods. Benchmark analyses also show that smaller objects, larger environments, and longer relocation distances pose greater challenges. The dataset and code are available here.

作者：Jiagan Huang, Zikun Zhou, Zijian Ni, Hongpeng Wang, Guangming Lu, Jun Yu, Wenjie Pei
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## 原始参考来源 (Sources)

- [arXiv 2610.10125：Lifelong small-object navigation in changing object layouts: a benchmark and method](https://arxiv.org/abs/2610.10125)