Identifying robust landmarks in feature-based maps

TL;DR

This paper presents a method for long-term maintenance of feature-based maps in urban environments by identifying and removing transient features, enhancing the robustness of vehicle localization over time.

Contribution

It introduces a scoring approach based on geometric distribution to eliminate ephemeral features and reduce dense maps, improving long-term map reliability.

Findings

Effective removal of transient features demonstrated over half a year of weekly drives.

Improved localization accuracy by maintaining stable feature sets.

Method suitable for long-term urban mapping and map density reduction.

Abstract

To operate in an urban environment, an automated vehicle must be capable of accurately estimating its position within a global map reference frame. This is necessary for optimal path planning and safe navigation. To accomplish this over an extended period of time, the global map requires long-term maintenance. This includes the addition of newly observable features and the removal of transient features belonging to dynamic objects. The latter is especially important for the long-term use of the map as matching against a map with features that no longer exist can result in incorrect data associations, and consequently erroneous localisation. This paper addresses the problem of removing features from the map that correspond to objects that are no longer observable/present in the environment. This is achieved by assigning a single score which depends on the geometric distribution and characteristics when the features are re-detected (or not) on different occasions. Our approach not only eliminates ephemeral features, but also can be used as a reduction algorithm for highly dense maps. We tested our approach using half a year of weekly drives over the same 500-metre section of road in an urban environment. The results presented demonstrate the validity of the long-term approach to map maintenance.