# Birds Eye View Look-Up Table Estimation with Semantic Segmentation
###### Abstract  
In this work, a study was carried out to estimate a look-up table (LUT) that converts a camera image plane to a birds eye view (BEV) plane using a single camera. The traditional camera pose estimation fields require high costs in researching and manufacturing autonomous vehicles for the future and may require pre-configured infra. This paper proposes an autonomous vehicle driving camera calibration system that is low cost and utilizes low infra. A network that outputs an image in the form of an LUT that converts the image into a BEV by estimating the camera pose under urban road driving conditions using a single camera was studied. We propose a network that predicts human-like poses from a single image. We collected synthetic data using a simulator, made BEV and LUT as ground truth, and utilized the proposed network and ground truth to train pose estimation function. In the progress, it predicts the pose by deciphering the semantic segmentation feature and increases its performance by attaching a layer that handles the overall direction of the network. The network outputs camera angle (roll/pitch/yaw) on the 3D coordinate system so that the user can monitor learning. Since the network's output is a LUT, there is no need for additional calculation, and real-time performance is improved.  
to construct a network that outputs the LUT by utilizing the feature points of the free space domain.  
A BEV was used to show depth using camera data intuitively. This BEV is similar to the around view monitoring (AVM) [7] used for parking assistance [8]. However, since an object in a position higher than the ground is projected onto the floor and expressed, any obstacle on the road surface is expressed as drooping compared to the original shape. Moreover, since camera-based depth lacks a reference distance, the relative distance is valid, but it cannot calculate the absolute distance. In general, the ToF [9] sensor, the size of the surrounding artificial landmark, and motion data of a driving vehicle are used to estimate actual distance. However, the purpose of this paper was to obtain the camera pose of a driving vehicle by using only a camera, which is a low-cost sensor, so it does not matter that the only output is the relative distance when using a BEV.  
Traditionally, to measure the pose of a camera, sensor calibration with a ToF sensor is performed, or the camera calibration process is performed in a tolerance calibration room [10], where a marker with a specified actual distance is located. However, the development of autonomous driving is progressing with the target of mass production, and it is necessary to set the cost of the sensor not too high so as to mass produce it. Therefore, we propose a system based on a single camera requiring other sensors and infra.  
The rest of this paper is organized as follows: Section 2 introduces related works; Section 3 discusses the DB used in the experiment; Section 4 describes the structure and details of the entire network utilized in machine learning; Section 5 presents experiments with different methods and their results; finally, Section 6 concludes the thesis and presents future work.