Traversable Region Estimation for Mobile Robots in an Outdoor Image

被引:0
作者
Sango Matsuzaki
Kimitoshi Yamazaki
Yoshitaka Hara
Takashi Tsubouchi
机构
[1] University of Tsukuba,Graduate School of Systems and Information Engineering
[2] Shinshu University,Faculty of Engineering
[3] Chiba Institute of Technology,Future Robotics Technology Center (fuRo)
来源
Journal of Intelligent & Robotic Systems | 2018年 / 92卷
关键词
Traversable region estimation; Mobile robot; Manually-instructed paths; Learning;
D O I
暂无
中图分类号
学科分类号
摘要
This paper describes a novel method to estimate appropriate traversable regions from an outdoor scene image. The traversable regions output by the proposed method reflect the common sense of people. For example, a candidate traversable region is “a paved road somewhat distant from the side ditch.” The input to the traversable region estimation is one color image. First, category is assigned to each pixel in the image. The categorization result is then input to the region estimator. Finally, the traversable region are estimated on the input image. An important aspect of this method is the application of two score functions in region estimation process. One score function places high value on categories selected as traversable paths by subjects. The other function places high value on categories that are not selected as traversable regions but are adjacent to categories with traversable paths. A combination of these two functions produces feasible estimation results. The effectiveness of the combined score functions was evaluated by experiments and a questionnaire.
引用
收藏
页码:453 / 463
页数:10
相关论文
共 30 条
[1]  
Borenstein J(1989)Real-time obstacle avoidance for fast mobile robots IEEE Trans. Syst. Man Cybern. 19 1179-1187
[2]  
Koren Y(1992)Exact robot navigation using artificial potential functions IEEE Trans. Robot. Autom. 8 501-518
[3]  
Rimon E(2004)Inevitable collision states a step towards safer robots Adv. Robot. 18 1001-1024
[4]  
Koditschek DE(2005)Obstacle detection and terrain classification for autonomous Off-Road navigation Auton. Robot. 18 81-102
[5]  
Fraichard T(2012)Identifying vegetation from laser data in structured outdoor environments Robot. Auton. Syst. 62 675-684
[6]  
Asama H(2009)Robot navigation in multi-terrain outdoor environments Int. J. Robot. Res. 28 685-700
[7]  
Manduchi R(2007)A human aware mobile robot motion planner IEEE Trans. Robot. 23 874-883
[8]  
Castano A(2005)A bayesian hierarchical model for learning natural scene categories Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) 2 524-531
[9]  
Talukder A(2004)Distinctive image features from scale-invariant keypoints Int. J. Comput. Vis. 60 91-110
[10]  
Matthies L(undefined)undefined undefined undefined undefined-undefined