CAJ | 학술논문

针对复杂交通场景中的室外移动机器人,提出了一种基于小波模极大值和集成学习支持向量机的障碍物检测力法.首先引入丫基十小波模极大值的奇异信号分析理论,对候选障碍物区域进行探测;然后,构建了一种基于集成学习改进的多分类支持向量机,对候选区域进行分类识别.实验中将该方法应用于多种交通场景(高速公路、城区道路),结果验证了其有效性、通用性和实时性.
침대복잡교통장경중적실외이동궤기인,제출료일충기우소파모겁대치화집성학습지지향량궤적장애물검측역법.수선인입아기십소파모겁대치적기이신호분석이론,대후선장애물구역진행탐측;연후,구건료일충기우집성학습개진적다분류지지향량궤,대후선구역진행분류식별.실험중장해방법응용우다충교통장경(고속공로、성구도로),결과험증료기유효성、통용성화실시성.
An obstacle detection method based on wavelet transform module maximum (WTMM) and ensemble learning support vector machine (SVM) is proposed for outdoor mobile robot in complex traffic scenes. Firstly, the WTMM-based singularity analysis theory is introduced into the regions of interest (ROIs) detection of candidate obstacles. Then, an im-proved multi-class SVM based on ensemble learning is constructed to classify and recognize these ROIs. In the experiment, the proposed method is applied to various traffic scenes (e.g., simple highway, complex urban street), and the result proves the validity, universality and real-time performance of the proposed method.