Micro-Localization: High-Precision Mobile Robot Localization Using Microscopic Ground Texture

Aaron Wilhelm, Marina de Albuquerque Marcon, Emith Uyanwatte, Nils Napp · N/A · 2026

Summary

We demonstrate high-precision global localization using natural ground texture viewed through microscope optics, achieving localization in the tens-of-micrometers regime.

Abstract Summary

We demonstrate high-precision global localization using natural ground texture viewed through microscope optics, achieving localization in the tens-of-micrometers regime. For the planar ground texture localization problem considered here, we relate optical magnification and image-space localization uncertainty to physical localization precision. Leveraging this relationship, we then develop a microscope-based localization system that addresses the calibration, camera-modeling, and focus challenges introduced by high-magnification imaging. We evaluate this system across several surface textures using a calibrated precision gantry. Across 1,007 held-out queries per surface, the system localizes every query and achieves translation RMSEs of 28$-$73 $\mu$m. Independent photometric registration agrees with the feature-based localization estimates to within approximately 9 $\mu$m RMSE. We further demonstrate localization on a low-cost mobile robot using the gantry-collected maps, and also show that the system can resolve micrometer-scale displacement caused by thermal expansion. These results show that microscopic natural surface texture can bring mobile localization into a precision regime typically associated with fixed positioning systems.

Key Points

We demonstrate high-precision global localization using natural ground texture viewed through microscope optics, achieving localization in the tens-of-micrometers regime.
For the planar ground texture localization problem considered here, we relate optical magnification and image-space localization uncertainty to physical localization precision.
Leveraging this relationship, we then develop a microscope-based localization system that addresses the calibration, camera-modeling, and focus challenges introduced by high-magnification imaging.
We evaluate this system across several surface textures using a calibrated precision gantry
Across 1,007 held-out queries per surface, the system localizes every query and achieves translation RMSEs of 28$-$73 $\mu$m.

System Design and Methodology

Ground texture localization is typically formulated using a downward-facing camera that observes a locally planar surface from an approximately fixed height. With the imaging geometry calibrated, the image pose within the surface map can be represented in SE(2) by planar translation and rotation.

Experiments and Results

We evaluate the proposed system on three surface textures: a printed wood-grain desk surface, terrazzo, and concrete. For each surface, we independently calibrate the gantry reference system and collect a localization database followed by a separate query set. The resulting mapped region spans approximately $83.5\times 30.4~\mathrm{cm}$, corresponding to roughly 3,700 non-overlapping native camera footprints.

Each surface is evaluated using the same set of 1,007 query poses spatially distributed throughout the mapped region.

Experiments and Results — Micro-Localization: High-Precision Mobile Robot Localization Using Microscopic Ground Texture — Fig. 2 : Macroscopic versus Microscopic Texture Characteristics . Macroscopic views (top) and corresponding microscopic views (bottom) of printed wood grain (left), concrete (middle), and terrazzo (right). The highlighted regions indicate the corresponding microscopic views, revealing distinctive surface structure even on visually uniform materials.

Sources and Demonstrations

Figures are reproduced from Wilhelm et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.