Dynamically Weighted Factor-Graph for Feature-based Geo-localization
Miguel Ángel Muñoz-Bañón Alejandro Olivas Edison Velasco-Sánchez Francisco A. Candelas Fernando Torres
Feature-based geo-localization relies on associating features extracted from
aerial imagery with those detected by the vehicle's sensors. This requires that
the type of landmarks must be observable from both sources. This no-variety of
feature types generates poor representations that lead to outliers and
deviations, produced by ambiguities and lack of detections respectively. To
mitigate these drawbacks, in this paper, we present a dynamically weighted
factor graph model for the vehicle's trajectory estimation. The weight
adjustment in this implementation depends on information quantification in the
detections performed using a LiDAR sensor. Also, a prior (GNSS-based) error
estimation is included in the model. Then, when the representation becomes
ambiguous or sparse, the weights are dynamically adjusted to rely on the
corrected prior trajectory, mitigating in this way outliers and deviations. We
compare our method against state-of-the-art geo-localization ones in a
challenging ambiguous environment, where we also cause detection losses. We
demonstrate mitigation of the mentioned drawbacks where the other methods fail.