让机器人像盲人一样用极少感知信息生成地图。
Map Imagination Like Blind Humans: Group Diffusion Model for Robotic Map Generation
- 基于群体扩散模型，仅凭路径数据生成点云地图。
- 结合极稀疏定位数据，地图精度显著提升。
- 适合传感器受限的机器人导航场景。
机器人能否像人类一样，在感知信息极为有限的情况下生成地图，尤其类比盲人依靠触觉等有限信息构建心智地图？为解决这一挑战，本文提出一种新型群体扩散模型（GDM）架构，使机器人仅依赖路径数据即可生成点云地图，无需视觉或深度数据。通过引入额外的极稀疏空间定位信息（如盲人获取的触觉定位），地图生成质量进一步提升。在公开数据集上的实验表明，该方法仅凭路径数据即可生成合理地图，并在融合少量LiDAR数据后生成更精细的地图。相比传统映射方法，本方法大幅降低对传感器的依赖，使机器人可在无重型传感设备情况下实现基础地图想象与生成。
原文摘要 · Abstract (English)
Can robots imagine or generate maps like humans do, especially when only limited information can be perceived like blind people? To address this challenging task, we propose a novel group diffusion model (GDM) based architecture for robots to generate point cloud maps with very limited input information.Inspired from the blind humans' natural capability of imagining or generating mental maps, the proposed method can generate maps without visual perception data or depth data. With additional limited super-sparse spatial positioning data, like the extra contact-based positioning information the blind individuals can obtain, the map generation quality can be improved even more.Experiments on public datasets are conducted, and the results indicate that our method can generate reasonable maps solely based on path data, and produce even more refined maps upon incorporating exiguous LiDAR data.Compared to conventional mapping approaches, our novel method significantly mitigates sensor dependency, enabling the robots to imagine and generate elementary maps without heavy onboard sensory devices.
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