Vision-based global localization in indoor environment with an object entity-based hybrid map

This paper presents a new object entity based global localization approach with stereo camera. A local invariant feature and stereo depth information arc used as visual features. The map we use here is a hybrid of global topological map and local object location map. The topological map includes some semantic information about the representing space and the object entities in the space. The object location map has the pose information of each object entity and visual features for object recognition. The localization process consists of two stages: coarse pose estimation and refined pose estimation. The coarse pose is computed by using the object recognition and point cloud fitting method. And the refined pose is estimated with particle filtering algorithm. An experiment shows that our approach can be an effective vision-based global localization method.