SignNav: Leveraging Signage for Semantic Visual Navigation in Large-Scale Indoor Environments
Proposes SignNav and START model, using spatial-temporal Transformer for semantic indoor navigation, achieving 80% success rate.
Key Findings
Methodology
This work introduces SignNav, a navigation task where robots interpret signage cues in large indoor spaces. The LSI-Dataset was built with 20 environments, providing diverse trajectories. The core model, START, integrates a spatial-aware module (based on ViT) to ground semantic hints into the environment, and a temporal-aware module (based on BERT) to incorporate long-range dependencies. Training involves a two-stage process: supervised teacher forcing and DAgger reinforcement, enhancing generalization. The architecture combines visual, semantic, and historical data for end-to-end decision-making.
Key Results
- In val-unseen environments, START achieved 80% success rate and 0.74 NDTW, outperforming rule-based and vision-language baselines. The average trajectory error was only 0.26 meters, demonstrating high precision. The model effectively handles dynamic cues and sparse signage, suitable for complex scenarios.
- Ablation studies confirmed the importance of spatial grounding and temporal memory modules, with performance drops of about 10% when removed. The two-stage training strategy improved success rate by roughly 10%. Real-world robot deployment validated robustness and practicality.
- The model maintains stable long-distance navigation, effectively coping with occlusion and signage absence, showing strong environment adaptability and decision continuity.
Significance
This research addresses the gap in semantic navigation within large-scale indoor environments, moving beyond traditional map-based or instruction-driven methods. The end-to-end learning approach enhances robot autonomy and robustness, enabling deployment in public spaces like airports and hospitals. It provides a scalable, mapless navigation solution, advancing the field of intelligent robotics and human-centric assistance.
Technical Contribution
The paper introduces a novel spatial-temporal Transformer architecture that combines visual grounding with long-term memory, enabling end-to-end semantic navigation. The creation of the LSI-Dataset offers a large, realistic benchmark. The training strategy effectively mitigates long-sequence bias, improving generalization. The system operates without pre-built maps, marking a significant step forward in autonomous indoor navigation.
Novelty
This is the first formal definition of SignNav, a signage-based end-to-end navigation task, leveraging a combined spatial-temporal Transformer. Unlike prior VLN or ObjectNav, it handles dynamic, sparse semantic cues without relying on maps or detailed instructions, representing a fundamental innovation in semantic navigation research.
Limitations
- The model's reliance on signage detection makes it vulnerable to occlusion or environmental changes affecting cue visibility.
- Training requires large-scale, diverse datasets, and performance in highly complex or unfamiliar environments remains to be validated.
- High computational cost during inference may limit real-time deployment on resource-constrained robots.
Future Work
Future efforts will focus on multi-modal integration, such as incorporating audio and tactile cues, to improve robustness. Developing adaptive signage detection and dynamic environment understanding will further enhance performance. Extending deployment to more complex, real-world scenarios and optimizing models for low-power hardware are also key directions.
AI Executive Summary
Indoor navigation in large, complex environments like airports and hospitals remains a significant challenge for autonomous robots. Traditional solutions depend heavily on pre-mapped layouts and step-by-step instructions, which are often impractical in dynamic settings. Humans naturally leverage semantic cues such as signage to navigate efficiently, but robots have yet to replicate this ability effectively.
This paper introduces the SignNav task, a novel benchmark for semantic visual navigation that requires robots to interpret signage cues—specifically directional arrows—and reason about subsequent actions. To support this, the authors constructed the Large-Scale Indoor Dataset (LSI-Dataset), comprising 20 realistic environments with procedurally injected signage, enabling diverse training and evaluation.
At the core of their approach is the Spatial-Temporal Aware Transformer (START), a deep learning architecture that integrates a spatial grounding module based on Vision Transformer (ViT) to map semantic hints into the environment, and a temporal module based on BERT to incorporate long-range dependencies. The model is trained via a two-stage process—initial supervised learning followed by DAgger reinforcement—to improve robustness and generalization.
Experimental results demonstrate that START achieves an 80% success rate and 0.74 NDTW on unseen environments, outperforming rule-based and vision-language baselines. The model also performs well in real-world robot deployments without pre-existing maps, showing promising practical applicability.
This work advances autonomous indoor navigation by enabling robots to understand and utilize environmental semantic cues dynamically. It opens avenues for deploying intelligent robots in public spaces, providing more flexible, mapless navigation solutions. Future work will explore multi-modal cues, environment adaptation, and real-time deployment optimizations, pushing the boundaries of autonomous semantic navigation further.
Deep Analysis
Background
近年来，机器人自主导航技术不断发展，ObjectNav和VLN等任务推动了视觉与语言理解的结合。ObjectNav专注于目标对象定位，VLN依赖逐步指令，但在大规模复杂公共空间中表现有限。随着深度学习和大规模数据的兴起，模型在环境理解上取得突破，但在动态语义提示和稀疏标识牌环境中仍存在挑战。标识牌作为人类导航的重要线索，尚未被充分利用，现有研究多停留在感知层面，缺乏端到端的解决方案。近年来，利用大规模场景数据和深度Transformer架构，推动了语义导航的研究，但仍需解决标识牌稀疏、动态变化带来的难题。
Core Problem
核心问题在于如何让机器人理解环境中的动态语义提示（如箭头）并将其映射到具体动作，尤其是在标识牌稀疏、遮挡频繁的场景中。传统方法多依赖预置地图或规则，难以应对环境的实时变化。实现无需预建地图的自主导航，要求模型具备长时记忆和空间映射能力，同时应对遮挡和环境变化带来的挑战。这对于公共空间的智能机器人应用具有重要意义，推动其在机场、医院等场景的落地。
Innovation
本研究的创新点包括：1）提出SignNav任务，定义了利用标识牌实现端到端导航的框架；2）构建了大规模LSI-Dataset，丰富了公共空间导航数据；3）设计了空间-时间Transformer（START），结合ViT和BERT架构，实现语义提示的空间映射和长时记忆；4）采用两阶段训练策略，有效缓解长序列偏差。这些创新突破了现有VLN和ObjectNav在环境复杂性和语义动态性方面的局限，为机器人自主导航提供了新思路。
Methodology
- �� 构建LSI-Dataset：采集20个真实场景的高保真3D模型，自动注入指示箭头，生成多样化轨迹。
- �� 定义SignNav任务：将导航定义为部分可观测的马尔可夫决策过程（POMDP），输入为RGB-D图像和局部语义提示，输出为离散动作（前进、转向、停止）。
- �� 模型架构：采用空间感知模块（ViT）将语义提示映射到视觉特征，时间感知模块（BERT）融合历史状态，形成全局记忆。
- �� 训练策略：先用教师引导进行监督学习，再用DAgger进行强化训练，提升泛化能力。
- �� 实验验证：在模拟环境和真实机器人平台上测试，评估成功率、轨迹误差等指标，进行消融分析确认各模块贡献。
Experiments
使用Habitat模拟器，划分训练集、验证集（已见和未见环境），共收集12000个轨迹。对比规则基、VLM和ViNT等方法，采用成功率（SR）、NDTW和SDTW指标。超参数包括学习率1e-5，批次12，模型深度6层。通过 ablation 实验验证空间和时间模块的重要性。模型在未见环境中表现优异，成功率达80%，轨迹误差仅0.26米，验证了其泛化能力和实用性。
Results
实验结果显示，START模型在未见环境中成功率明显优于基线（80%对70%），NDTW指标也高出0.05以上。消融实验表明，去除空间感知或时间记忆模块，性能下降约10%。模型在长距离导航中表现稳定，能有效应对标识牌缺失和遮挡，验证了其环境适应性和决策连续性。真实平台部署进一步验证了模型的实用价值。
Applications
该技术可应用于机场、医院等公共空间的自主导航机器人，实现无需预建地图的高效导航。只需环境中存在指示箭头，机器人即可自主判断路径，提升服务效率。未来还可结合多模态信息（声音、触觉）增强环境理解，推动智能机器人在复杂场景中的广泛应用。
Limitations & Outlook
模型对标识牌检测的依赖较强，环境中标识牌变化或遮挡会影响性能。训练依赖大规模数据，泛化能力在极端复杂场景下仍需验证。实际部署时对计算资源要求较高，未来需优化模型结构以适应低功耗平台。
Plain Language Accessible to non-experts
想象你在一个大型商场里找出口。商场里没有明确的地图，但你会注意到墙上的箭头和标识牌，告诉你应该往哪个方向走。你记得之前看到的箭头指向出口的位置，遇到拐角时会回忆起之前的标识，帮助你继续前行。机器人也是这样，它通过观察环境中的箭头，结合之前看到的线索，逐步找到目标地点。这个过程就像你用箭头和记忆来导航一样，机器人用特殊的算法把这些线索变成行动指南，帮助它在复杂的空间中自主找到出口。
ELI14 Explained like you're 14
想象你在一个超大商场玩寻宝游戏，没有地图，但墙上有箭头指示方向。你会注意到箭头，记住它们的位置，然后根据箭头的指向走，遇到拐角时还会想起之前看到的箭头，继续走。机器人也是这样，它会用眼睛“看”环境中的箭头，然后用脑子“记”之前看到的箭头，结合这些信息一步步找到目标地点。这个过程就像你用箭头和记忆玩迷宫游戏一样，机器人用聪明的算法，把箭头变成行动指南，帮它在大空间里找到目标。
Abstract
Humans routinely leverage semantic hints provided by signage to navigate to destinations within novel Large-Scale Indoor (LSI) environments, such as hospitals and airport terminals. However, this capability remains underexplored within the field of embodied navigation. This paper introduces a novel embodied navigation task, SignNav, which requires the agent to interpret semantic hint from signage and reason about the subsequent action based on current observation. To facilitate research in this domain, we construct the LSI-Dataset for the training and evaluation of various SignNav agents. Dynamically changing semantic hints and sparse placement of signage in LSI environments present significant challenges to the SignNav task. To address these challenges, we propose the Spatial-Temporal Aware Transformer (START) model for end-to-end decision-making. The spatial-aware module grounds the semantic hint of signage into physical world, while the temporal-aware module captures long-range dependencies between historical states and current observation. Leveraging a two-stage training strategy with Dataset Aggregation (DAgger), our approach achieves state-of-the-art performance, recording an 80% Success Rate (SR) and 0.74 NDTW on val-unseen split. Real-world deployment further demonstrates the practicality of our method in physical environment without pre-built map.