SOURCE-LINKED INTELLIGENCE

Odometer-Agnostic Drift Correction Using OpenStreetMap Lane Geometry

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Despite significant progress in odometry estimation, long-term drift remains a fundamental limitation of incremental pose integration, especially in large-scale or loop-free environments. Existing map-assisted methods can reduce drift, but often depend on dense maps, sensor-specific processing, or complex matching pipelines. We propose a lightweight open-source, odometry-agnostic correction method that aligns short trajectory segments to OpenStreetMap (OSM) lane centerlines. By formulating drift correction as a direct alignment between recent odometry and sparse lane geometry, the method enabl

Read original source ↗ Open in workspace

recordType

paper

region

Global