저채널 3차원 라이다를 이용한 폴라뷰 기반의 객체인식 알고리즘 (Polar-View Based Object Detection Algorithm Using 3D Low-Channel Lidar)

서지정보

· 발행기관 : 제어·로봇·시스템학회
· 수록지 정보 : 제어.로봇.시스템학회 논문지 / 25권 / 1호 / 56 ~ 62페이지
· 저자명 : 권순섭, 박태형

초록

In order for an autonomous vehicle to move, object detection is required to recognize the surrounding environment. The sensors used for object detection mainly use a camera and lidar. However, it is difficult to detect the camera because of its influence on the surrounding environment. Therefore, object detection using lidar is required. For lidar-based object detection, we mainly use a high-channel lidar with a high resolution. However, high-channel lidar is expensive and is difficult to commercialize. To solve this problem, object detection studies using low-channel lidar are underway. In this paper, we present an algorithm to find an object (vehicle or pedestrian) using three 3D low-channel lidar systems. First, we converted the data from the lidar to the polar view. Then, we input the converted polar view into YOLO v3 to predict the class and the region of interest (ROI) of the object. We used K-means to separate the background and the object from the image in the predicted ROI to find the object except for the background. Only the object area found last was converted back into 3D space to find the location of the object.

영어초록

In order for an autonomous vehicle to move, object detection is required to recognize the surrounding environment. The sensors used for object detection mainly use a camera and lidar. However, it is difficult to detect the camera because of its influence on the surrounding environment. Therefore, object detection using lidar is required. For lidar-based object detection, we mainly use a high-channel lidar with a high resolution. However, high-channel lidar is expensive and is difficult to commercialize. To solve this problem, object detection studies using low-channel lidar are underway. In this paper, we present an algorithm to find an object (vehicle or pedestrian) using three 3D low-channel lidar systems. First, we converted the data from the lidar to the polar view. Then, we input the converted polar view into YOLO v3 to predict the class and the region of interest (ROI) of the object. We used K-means to separate the background and the object from the image in the predicted ROI to find the object except for the background. Only the object area found last was converted back into 3D space to find the location of the object.

참고자료

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