Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras

TL;DR

This paper presents a trained trajectory automated parking system that uses Visual SLAM with surround view cameras to build persistent maps for improved vehicle re-localization in frequent parking scenarios.

Contribution

It introduces a trained trajectory parking system integrating Visual SLAM for persistent mapping and re-localization, deployed on commercial vehicles for practical use.

Findings

System successfully deployed on commercial vehicles

Enhanced re-localization accuracy in frequent parking scenarios

Demonstrated practical application through consumer video

Abstract

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.