Lightweight Ground Texture Localization

Abstract

We present a lightweight ground texture based localization algorithm (L-GROUT) that improves the state of the art in performance and can be run in real-time on single board computers without GPU acceleration. Such computers are ubiquitous on small indoor robots and thus this work enables high-precision, millimeter-level localization without instrumenting, marking, or modifying the environment. The key innovations are an improved database feature extraction algorithm, a dimensionality reduction method based on locality preserving projections (LPP) that can accommodate faster-to-compute binary features, and an improved spatial filtering step that better preserves performance when the databases are tuned for lightweight applications. We demonstrate the approach by running the whole system on a low-cost single board computer (Raspberry Pi 4) to produce global localization estimates at greater than 4Hz on an outdoor asphalt dataset.