Trained Trajectory based Automated Parking System using Visual SLAM
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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 this paper, we discuss the use cases, design and implementation of a trained trajectory automated parking system. To encourage further research, we release a dataset of 50 video sequences comprising of over 100,000 images with the associated ground truth as a companion to our WoodScape dataset <cit.>. To the best of the authors' knowledge, this is the first public dataset for trained trajectory parking system scenarios.
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