## 1 Introduction
However, though semantic SLAM has achieved state-of-the-art performance in various practical applications, the purely data-driven-based neural network model has also shown many disadvantages, such as the non-explainability issue and the huge amount of labeled data requirement, both of which limit its adaptability in different environments, especially the human beings involved and safety-critical scenarios [23; 24]. Therefore, in this work, we hope to resolve the aforementioned problem partially by embedding rule-based and explainable structures, allowing the robot to be able to adapt to the new environment efficiently. In practical applications, in order to be able to achieve the all-around perceptual coverage of objects in any direction in the task scene, mobile robots often need to be equipped with multiple vision sensors to meet this demand. In smart cars, for example, the vision system on board often consists of multiple high-resolution cameras with different viewing angles to cover a 360-degree field of view around the vehicle. Having a wider field of view means that the robots can observe more information about the environment, but it also means that the amount of computation required to process this information is greatly increased. In addition, under the current situation that the perception method based on the deep neural network model has become the mainstream, more raw perception data input also require corresponding orders of magnitude labeled data, which undoubtedly greatly increases the cost and time of the perception model when adapting to new scenarios, which is not conducive to rapid deployment and application in new scenarios. In summary, the main contributions of this article are summarized as follows:* We propose a multi-level semantic-structure-aware global orientation estimation framework, which consists of a semantic information extraction module and a global orientation estimation module. * In the semantic information extraction stage, we attempted to process the surround view synthesized images obtained after the inverse perspective mapping (IPM) of the original image and use the passable area segmentation mode, which more easily obtains the annotation data, fully combines the potential prior information in the task, and obtains the boundary including the passable area. The semantic information including visual feature points and ground marking edges in the passable area can effectively reduce the requirements of the semantic perception model for labeled data and, finally, meet the needs for tasks such as mapping and positioning. * In the orientation estimation stage, we designed a segmentation method for marker lines based on the structural rules of the Manhattan world, which can be used to obtain from the image a collection of line segments that conform to the structural assumptions of Manhattan and the dominant orientation of these lines; thus, we can distinguish the marker lines from the noise lines. * We validated the effectiveness of the proposed scheme by taking the semantic perception task of intelligent vehicles equipped with multi-vision systems in the automatic valet parking task as an example. The remainder of this work is organized as follows: In Section 2, some related works are described. In Section 3, we describe our system architecture, followed by semantic information extraction in Section 4. After that, we present the semantic-aware global orientation estimation method in Section 5, with the experiments presented in Section 6 and the conclusion in Section 7.