Odometer-Agnostic Drift Correction Using OpenStreetMap Lane Geometry

2026-09-09

AI Summary

本文提出了一种利用OpenStreetMap车道几何信息来校正长期漂移问题的轻量级方法，适用于各种里程计且无需密集地图或复杂预处理。

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.

Problem

• long-term drift
• odometry estimation
• OpenStreetMap
• lane geometry
• incremental pose integration

Innovation

• odometry-agnostic
• OpenStreetMap lane geometry
• drift correction
• lightweight method