Context-Aware Recognition of Drivable Terrain with Automated Parameters Estimation

被引:0
|
作者
Wietrzykowski, Jan [1 ]
Skrzypczynski, Piotr [1 ]
机构
[1] Poznan Univ Tech, Inst Control Robot & Informat Engn, Ul Piotrowo 3A, PL-60965 Poznan, Poland
来源
INTELLIGENT AUTONOMOUS SYSTEMS 15, IAS-15 | 2019年 / 867卷
关键词
Vision; Terrain classification; Conditional Random Fields; CLASSIFICATION;
D O I
10.1007/978-3-030-01370-7_49
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper deals with the terrain classification problem for autonomous service robots in semi-structured outdoor environments. The aim is to recognize the drivable terrain in front of a robot that navigates on roads of different surfaces, avoiding areas that are considered non-drivable. Since the system should be robust to such factors as changing lighting conditions, mud and fallen leaves, we employ multi-sensor perception with a monocular camera and a 2D laser scanner. The labeling of the terrain obtained from a Random Trees classifier is refined by context-aware inference using the Conditional Random Field. We demonstrate that automatic learning of the parameters for Conditional Random Fields improves results in comparison to similar approaches without the context-aware inference or with parameters set by hand.
引用
收藏
页码:626 / 638
页数:13
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