3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes
3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes
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DOI：
10.1016/j.robot.2016.06.007
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发表时间：
2016-09-01
影响因子：
4.3
通讯作者：
Nunes, Urbano
中科院分区：
文献类型：
--
作者：
Asvadi, Alireza;Premebida, Cristiano;Nunes, Urbano
在自动驾驶的背景下，人工感知是指智能系统将感知数据转化为车辆周围环境的有效模型的过程。本文以智能车辆上安装的3D- lidar数据为基础，提出了一种基于体素与平面的城市环境地面建模与障碍物检测三维感知系统。该系统包含时间相关数据，由两个主要模块组成：(i)使用分段平面拟合算法和ransac方法进行有效的地面估计，以及（ii）使用判别分析和自我运动信息进行静态和移动障碍物检测的体素网格模型。这种感知系统可以直接应用于智能车辆的安全系统，特别是在避免碰撞和检测弱势道路使用者（即行人和骑自行车的人）方面。实验使用来自Velodyne激光雷达的点云数据和来自惯性导航系统的定位数据，对静态/移动障碍物检测模块和表面估计方法进行了定量和定性评估。使用KITTI数据库的实验结果表明，该方法在城市场景中的适用性和有效性。(C) 2016 Elsevier B.V.版权所有
Artificial perception, in the context of autonomous driving, is the process by which an intelligent system translates sensory data into an effective model of the environment surrounding a vehicle. In this paper, and considering data from a 3D-LIDAR mounted onboard an intelligent vehicle, a 3D perception system based on voxels and planes is proposed for ground modeling and obstacle detection in urban environments. The system, which incorporates time-dependent data, is composed of two main modules: (i) an effective ground surface estimation using a piecewise plane fitting algorithm and RANSAC-method, and (ii) a voxel-grid model for static and moving obstacles detection using discriminative analysis and ego-motion information. This perception system has direct application in safety systems for intelligent vehicles, particularly in collision avoidance and vulnerable road users detection, namely pedestrians and cyclists. Experiments, using point-cloud data from a Velodyne LIDAR and localization data from an Inertial Navigation System were conducted for both a quantitative and a qualitative assessment of the static/moving obstacle detection module and for the surface estimation approach. Reported results, from experiments using the KITTI database, demonstrate the applicability and efficiency of the proposed approach in urban scenarios. (C) 2016 Elsevier B.V. All rights reserved.