Super-sensor for 360-degree environment perception: Point cloud segmentation using image features

Abstract

This paper describes a super-sensor that enables 360-degree environment perception for automated vehicles in urban traffic scenarios. We use four fisheye cameras, four 360-degree LIDARs and a GPS/IMU sensor mounted on an automated vehicle to build a super-sensor that offers an enhanced low-level representation of the environment by harmonizing all the available sensor measurements. Individual sensors cannot provide a robust 360-degree perception due to their limitations: field of view, range, orientation, number of scanning rays, etc. The novelty of this work consists of segmenting the 3D LIDAR point cloud by associating it with the 2D image semantic segmentation. Another contribution is the sensor configuration that enables 360-degree environment perception. The following steps are involved in the process: calibration, timestamp synchronization, fisheye image unwarping, motion correction of LIDAR points, point cloud projection onto the images and semantic segmentation of images. The enhanced low-level representation will improve the high-level perception environment tasks such as object detection, classification and tracking.