用可渲染神经辐射图实现真实世界机器人视觉导航，定位成功率提升近70%。
RNR-Nav: A Real-World Visual Navigation System Using Renderable Neural Radiance Maps
- 基于改进的RNR-Map++构建鸟瞰图，减少信息丢失。
- 无渲染粒子滤波定位，实现实时高精度定位。
- 在真实场景中导航成功率达84.4%，优于原方法68.8%。
我们提出一种新型视觉定位与导航框架，直接将真实环境中的观测视觉信息融入鸟瞰图。尽管可渲染神经辐射图（RNR-Map）在模拟环境中表现优异，但在真实场景部署仍面临未被发现的挑战。原RNR-Map通过多向量投影生成单一隐变量，导致次优条件下信息损失。为此，我们提出的RNR-Map++引入加权地图与位置编码策略以缓解信息丢失。为实现鲁棒实时定位，我们在无需渲染的关联式定位框架中集成粒子滤波器。最终构建了基于RNR-Map++的真实世界机器人导航系统RNR-Nav。实验表明，该方法显著提升渲染质量与定位鲁棒性；在真实导航任务中，RNR-Nav成功率达84.4%，较原始论文方法提升68.8%。
原文摘要 · Abstract (English)
We propose a novel visual localization and navigation framework for real-world environments directly integrating observed visual information into the bird-eye-view map. While the renderable neural radiance map (RNR-Map) shows considerable promise in simulated settings, its deployment in real-world scenarios poses undiscovered challenges. RNR-Map utilizes projections of multiple vectors into a single latent code, resulting in information loss under suboptimal conditions. To address such issues, our enhanced RNR-Map for real-world robots, RNR-Map++, incorporates strategies to mitigate information loss, such as a weighted map and positional encoding. For robust real-time localization, we integrate a particle filter into the correlation-based localization framework using RNRMap++ without a rendering procedure. Consequently, we establish a real-world robot system for visual navigation utilizing RNR-Map++, which we call "RNR-Nav." Experimental results demonstrate that the proposed methods significantly enhance rendering quality and localization robustness compared to previous approaches. In real-world navigation tasks, RNR-Nav achieves a success rate of 84.4%, marking a 68.8% enhancement over the methods of the original RNR-Map paper.
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