Visual SLAM in Long-Range Autonomous Parking Application Based on Instance-Aware Semantic Segmentation via Multi-Task Network Cascades and Metric Learning Scheme

Long-range Autonomous Parking is becoming an attractive application in terms of demands. The vehicle is capable of driving autonomously into the appointed parking slot when the driver leaves it at the drop-off spot. In this application, the ability of accurate localization has become a key issue, especially in GPS-denied environments. This paper proposes a method of localization and mapping for Long-range Autonomous Parking, which is achieved by Visual SLAM based on deep learning algorithms. Firstly, we propose an instance segmentation via multi-task network cascades, and even in a complex visual environment, the main roadway instances of interest in the parking lot IPM image can be detected, such as parking corners, speed bumps. Then we combine the information of wheel encoders to build a global semantic map of the parking lot. Vehicles can often rely on semantic map matching to achieve high-precision localization. However, without a good initial position, it is difficult to infer an accurate position by matching the semantic map, such as randomly selecting entrances to enter the parking lot. Therefore, we propose an area feature network based on metric learning to extract features that distinguish different areas and infer the approximate initial position of the vehicle. Specifically, we extract features from the images of the surround-view cameras, use the vehicle position as weak supervision, and finally construct an area feature map. In summary, our proposed method provides accurate vehicle localization and parking lot maps for Long-range Autonomous Parking.