Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras
This addresses the need for more efficient and reliable automated parking in vehicles, particularly for frequent parking scenarios like home or office, though it appears incremental as it builds on existing parking systems by adding Visual SLAM.
The paper tackles the problem of automated parking by introducing a trained trajectory system that uses Visual SLAM on surround view cameras to build persistent maps for re-localization, and it has been deployed on commercial vehicles with a consumer application demonstrated.
Automated Parking is becoming a standard feature in modern vehicles. Existing parking systems build a local map to be able to plan for maneuvering towards a detected slot. Next generation parking systems have an use case where they build a persistent map of the environment where the car is frequently parked, say for example, home parking or office parking. The pre-built map helps in re-localizing the vehicle better when its trying to park the next time. This is achieved by augmenting the parking system with a Visual SLAM pipeline and the feature is called trained trajectory parking in the automotive industry. In this paper, we discuss the use cases, design and implementation of a trained trajectory automated parking system. The proposed system is deployed on commercial vehicles and the consumer application is illustrated in \url{https://youtu.be/nRWF5KhyJZU}. The focus of this paper is on the application and the details of vision algorithms are kept at high level.