Evaluation of Systematic and Random Errors in Occupancy Grid Maps

Yuguang Liu, Marko Radanovic, Krista A. Ehinger, Kourosh Khoshelham

Abstract. Map evaluation for occupancy grid mapping (OGM) is critical in the field of high-definition mapping of the road environment for autonomous vehicles. Existing methods cannot adequately evaluate the systematic and random errors that might be present in OGM. This article introduces two evaluation metrics for OGM under LiDAR position uncertainty: Mean Signed Distance (MSD) and Mean Absolute Deviation (MAD). MSD quantifies systematic displacement of occupied cells, while MAD measures random error exhibited as boundary thickening. Unlike classification-based, probabilistic, and geometric metrics, MSD and MAD directly isolate displacement and thickening effects in OGM. We validate both metrics in a controlled synthetic environment and on a real indoor LiDAR dataset, showing better performance than conventional metrics.