基于激光雷达的无人驾驶汽车动态障碍物检测、跟踪与识别方法  

Lidar Based Dynamic Obstacle Detection, Tracking and Recognition Method for Driverless Cars

作　　者：黄如林[1,2] 梁华为[2] 陈佳佳[2] 赵盼[2] 杜明博 

机构地区：[1]中国科学技术大学自动化系,安徽合肥230061 [2]中国科学院合肥物质科学研究院应用技术研究所,安徽合肥230088

出　　处：《机器人》2016年第4期437-443,共7页Robot

基　　金：国家自然科学基金(91120307;91320301;91420104);国家自然科学基金青年基金(61304100)

摘　　要：现有的基于几何特征的动态障碍物检测跟踪方法误检率较高,基于动态障碍物几何特征和运动状态的识别方法受距离和扫描角度影响较大,无法满足真实交通场景应用的要求.针对这些不足,本文提出了一种基于多特征融合的动态障碍物检测与跟踪方法和一种基于时空特征向量的动态障碍物识别方法.首先在动态障碍物几何特征的基础上,考虑障碍物回波脉冲宽度特征,以提高障碍物检测跟踪的正确率;其次,基于障碍物时间维度与空间维度信息来构建时空特征向量,并进而采用支持向量机方法实现动态障碍物的识别,以提升障碍物识别的准确率.最后,通过实车试验对所提出方法的准确性和有效性进行了验证.Existing methods for dynamic obstacle detection and tracking have lots of shortages, such as the high false detection rate for geometric characteristics based methods, and severe influences of scanning angle and/or distance on features and motion based methods. All these issues make existing methods can＇t satisfy the requirements of real traffic scenarios. Targeting at aforementioned disadvantages, two methods are proposed： a multi-feature fusion based method for dynamic obstacle detection and tracking, and a spatial-temporal characteristic vector based method for obstacle recognition. Firstly, the echo pulse width of obstacles is considered based on the geometric features of dynamic obstacles to increase the accuracy of detection and tracking results. Secondly, a spatial-temporal feature vector is constructed with considering time dimension and spatial dimension information of obstacles. Thus, the SVM （support vector machine） method is used to identify dynamic obstacles to improve the recognition accuracy. Finally, the accuracy and validity of the proposed methods are verified by an autonomous vehicle in real traffic environments.

关 键 词：动态障碍物检测 跟踪与识别 回波脉冲宽度 时空特征向量 支持向量机 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]