计算机科学
激光雷达
人工智能
分割
卷积神经网络
点云
帧速率
代表(政治)
计算机视觉
深度学习
RGB颜色模型
编码(集合论)
测距
语义学(计算机科学)
遥感
地质学
政治
电信
集合(抽象数据类型)
程序设计语言
法学
政治学
作者
Andres Milioto,Ignacio Vizzo,Jens Behley,Cyrill Stachniss
标识
DOI:10.1109/iros40897.2019.8967762
摘要
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high-quality deep learning algorithms for image-based recognition, high-level semantic perception tasks are pre-dominantly solved using high-resolution cameras. As a result of that, other sensor modalities potentially useful for this task are often ignored. In this paper, we push the state of the art in LiDAR-only semantic segmentation forward in order to provide another independent source of semantic information to the vehicle. Our approach can accurately perform full semantic segmentation of LiDAR point clouds at sensor frame rate. We exploit range images as an intermediate representation in combination with a Convolutional Neural Network (CNN) exploiting the rotating LiDAR sensor model. To obtain accurate results, we propose a novel post-processing algorithm that deals with problems arising from this intermediate representation such as discretization errors and blurry CNN outputs. We implemented and thoroughly evaluated our approach including several comparisons to the state of the art. Our experiments show that our approach outperforms state-of-the-art approaches, while still running online on a single embedded GPU. The code can be accessed at https://github.com/PRBonn/lidar-bonnetal.
科研通智能强力驱动
Strongly Powered by AbleSci AI