Align trajectory data to OpenStreetMap lanes to cut odometry errors
Odometer-Agnostic Drift Correction Using OpenStreetMap Lane Geometry
Robotics
Summary
Navigation systems that track movement often accumulate small mistakes over time, making their position less accurate. The authors propose a way to fix these errors by matching recent movement paths to the known shapes of lanes from OpenStreetMap, a freely available map database. This approach works with different types of movement data and doesn’t need complicated maps or extra processing. Tests show it improves position accuracy, especially when errors become large.
What this means in practice
- •For autonomous vehicle engineers: Improve vehicle localization by aligning incremental odometry to public road lane data for more reliable navigation without heavy mapping.
- •For robotics system developers: Integrate lightweight drift correction in robots operating over large or loop-free areas by using sparse lane information to reduce position errors online.
Authors
Joaquin Caballero, Emilio Garcia-Fidalgo, Alberto Ortiz, Jarno Ralli
Abstract
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 enables efficient online operation without dense priors or expensive preprocessing. Experiments with LiDAR and visual odometry backends demonstrate consistent improvements, with particularly strong gains under severe drift.