In recent years, object-oriented simultaneous localization and mapping (SLAM)
has attracted increasing attention due to its ability to provide high-level
semantic information while maintaining computational efficiency. Some
researchers have attempted to enhance localization accuracy by integrating the
modeled object residuals into bundle adjustment. However, few have demonstrated
better results than feature-based visual SLAM systems, as the generic coarse
object models, such as cuboids or ellipsoids, are less accurate than feature
points. In this paper, we propose a Visual Object Odometry and Mapping
framework VOOM using high-level objects and low-level points as the
hierarchical landmarks in a coarse-to-fine manner instead of directly using
object residuals in bundle adjustment. Firstly, we introduce an improved
observation model and a novel data association method for dual quadrics,
employed to represent physical objects. It facilitates the creation of a 3D map
that closely reflects reality. Next, we use object information to enhance the
data association of feature points and consequently update the map. In the
visual object odometry backend, the updated map is employed to further optimize
the camera pose and the objects. Meanwhile, local bundle adjustment is
performed utilizing the objects and points-based covisibility graphs in our
visual object mapping process. Experiments show that VOOM outperforms both
object-oriented SLAM and feature points SLAM systems such as ORB-SLAM2 in terms
of localization. The implementation of our method is available at
this https URL

使用高级对象和低级点作为分层地标，采用改进的观测模型和新的数据关联方法，并在粗到精的方式下，利用对象信息增强特征点的数据关联并更新地图，最终优化相机位置和对象，实验证明在定位方面优于面向对象的 SLAM 和特征点的 SLAM 系统。