仅靠触觉实现高精度实时3D物体定位与重建
GelSLAM: A Real-time, High-Fidelity, and Robust 3D Tactile SLAM System
- 用触觉感知的表面法向和曲率替代点云进行跟踪
- 实现亚毫米级形状重建，长时间追踪误差小
- 适合抓握、操作等需要精确触觉反馈的任务
准确感知物体位姿与形状对精准抓取和操作至关重要。相比常见的视觉方法，触觉感知在接触状态下具有更高的精度且不受遮挡影响，特别适用于手内操作等高精度任务。本文提出GelSLAM，一种完全依赖触觉的实时3D SLAM系统，可长期稳定估计物体位姿并以高保真度重建物体形状。不同于传统点云方法，GelSLAM利用触觉提取的表面法向量和曲率实现鲁棒跟踪与回环检测。系统可在实时条件下实现低误差、低漂移的运动追踪，并对木制工具等低纹理物体实现亚毫米级形状重建。该系统将触觉感知从局部接触扩展至全局、长时程空间感知，有望成为多种交互式高精度操作任务的基础。视频演示、代码及数据集详见https://joehjhuang.github.io/gelslam。
原文摘要 · Abstract (English)
Accurately perceiving an object's pose and shape is essential for precise grasping and manipulation. Compared to common vision-based methods, tactile sensing offers advantages in precision and immunity to occlusion when tracking and reconstructing objects in contact. This makes it particularly valuable for in-hand and other high-precision manipulation tasks. In this work, we present GelSLAM, a real-time 3D SLAM system that relies solely on tactile sensing to estimate object pose over long periods and reconstruct object shapes with high fidelity. Unlike traditional point cloud-based approaches, GelSLAM uses tactile-derived surface normals and curvatures for robust tracking and loop closure. It can track object motion in real time with low error and minimal drift, and reconstruct shapes with submillimeter accuracy, even for low-texture objects such as wooden tools. GelSLAM extends tactile sensing beyond local contact to enable global, long-horizon spatial perception, and we believe it will serve as a foundation for many precise manipulation tasks involving interaction with objects in hand. The video demo, code, and dataset are available at https://joehjhuang.github.io/gelslam.
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