Visual Localization in Repetitive and Symmetric Indoor Parking Lots using 3D Key Text Graph

Indoor parking lots are the GPS-denied spaces to which vision-based localization approaches have usually been applied to solve localization problems. However, due to the repetitiveness and symmetry of the spaces, visual localization methods commonly confront difficulties in estimating precise 3D poses. In this study, we propose four novel modules that improve localization precision by imposing the existing methods with the spatial discerning ability. The first module constructs a key text graph that represents the topology of key texts in the space and becomes the basis for discerning repetitiveness and symmetry. Next, the orientation filtering module estimates the unknown 3D orientation of the query image and resolves spatial symmetric ambiguity. The similarity scoring module sorts out the top-scored database images, discerning the spatial repetitiveness based on detected key text bounding boxes. Our pose verification module evaluates the pose confidence of top-scored candidates and determines the most reliable pose. Our method has been validated in two real indoor parking lots, achieving new state-of-the-art performance levels.