An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoo...An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.展开更多
r-learning,which is based on e-learning and u-learning,is defined as a learning support system that intelligent robots serve verbal and nonverbal interactions on ubiquitous computing environment.In order to guarantee ...r-learning,which is based on e-learning and u-learning,is defined as a learning support system that intelligent robots serve verbal and nonverbal interactions on ubiquitous computing environment.In order to guarantee the advantages of r-learning contents with no limits of timc and place and with nonverbal interaction which are not in e-learning contents,in recent years,assessment criteria for r-learning contents are urgently rcquired.Therefore,the reliable and valid assessment criteria were developed for nonverbal interaction contents in r-learning,and its detailed research content is as follows.First,assessment criteria for nonverbal interaction in r-learning contents will be specified into gesture,facial expression,semi-verbal message,distance,physical contact and time.Second,the validity of the developed assessment criteria will be proved by statistics.Consequently,the assessment criteria for nonverbal interaction contents will be helpful when choosing the better r-learning content and producing the better r-learning content,and the reliability of school education is improved ultimately.展开更多
文摘An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.
基金Project(2011)supported by the research grant of the Chungbuk National University,South Korea
文摘r-learning,which is based on e-learning and u-learning,is defined as a learning support system that intelligent robots serve verbal and nonverbal interactions on ubiquitous computing environment.In order to guarantee the advantages of r-learning contents with no limits of timc and place and with nonverbal interaction which are not in e-learning contents,in recent years,assessment criteria for r-learning contents are urgently rcquired.Therefore,the reliable and valid assessment criteria were developed for nonverbal interaction contents in r-learning,and its detailed research content is as follows.First,assessment criteria for nonverbal interaction in r-learning contents will be specified into gesture,facial expression,semi-verbal message,distance,physical contact and time.Second,the validity of the developed assessment criteria will be proved by statistics.Consequently,the assessment criteria for nonverbal interaction contents will be helpful when choosing the better r-learning content and producing the better r-learning content,and the reliability of school education is improved ultimately.
