Camera Switching 및 차량의 주행 방향을 활용한 자율 발렛 주차

Abstract

Autonomous valet parking(AVP) is a system that allows a vehicle to detect a parking space and park without any human intervention. This paper explores the efficient use of monocular visual simultaneous localization and mapping(visual-SLAM) in AVP scenarios, while also highlighting several problems arising from current AVP methods. These problems include the difficulties of performing a loop closure in narrow places and the unreliability of the estimated scale between the real-world and SLAM maps. This paper explores two methods for resolving these problems. The first solution is to perform AVP without loop closure. This can be done through a camera switching method that uses the front camera while parking, and the rear camera in return driving scenarios. The second solution involves making use of an alternative automatic parking algorithm that utilizes a car’s heading. Most automatic parking algorithms utilize monocular visual-SLAM, but it is difficult to calculate an accurate scale using this method. The alternative automatic parking algorithm using a car’s heading could eliminate these issues with scaling. Each method was tested in three different locations, returning average success rates of 80% in return driving and 93.3% in automatic parking. The methods highlighted in this paper only utilize cameras, which are inexpensive compared to other sensors and are expected to contribute to commercializing the AVP system.