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.

翻译：准确感知物体的姿态与形状对于实现精确抓取与操作至关重要。与常见的基于视觉的方法相比，触觉感知在追踪和重建接触物体时，具有精度高且不受遮挡影响的优势。这使其在手持操作及其他高精度操控任务中具有特殊价值。本研究提出GelSLAM，一种完全依赖触觉感知的实时3D SLAM系统，能够长时间估计物体姿态并以高保真度重建物体形状。不同于传统的基于点云的方法，GelSLAM利用触觉衍生的表面法向量与曲率实现鲁棒的追踪与闭环检测。该系统能够以低误差和极小漂移实时追踪物体运动，并以亚毫米级精度重建形状，即使对于木质工具等低纹理物体亦能胜任。GelSLAM将触觉感知从局部接触扩展至全局、长时程的空间感知，我们相信它将为许多涉及手持物体交互的精确操控任务奠定基础。视频演示、代码及数据集可在 https://joehjhuang.github.io/gelslam 获取。