{"title":"Detection of moving features using IMU-camera without knowing both the initial conditions and gravity direction","authors":"Jwusheng Hu, Chin-Yuan Tseng, Ming-yuan Chen","doi":"10.1109/CACS.2013.6734149","DOIUrl":"https://doi.org/10.1109/CACS.2013.6734149","url":null,"abstract":"Detecting moving features relative to ground in the images of a moving camera is important for mobile robot localization in practice. This problem is particularly difficult if the initial conditions of the camera are unknown. In this paper, we propose a moving feature detection method by using a calibrated IMU-camera in a dynamic environment. The proposed method is able to separate static and dynamic features without knowing the IMU-camera initial conditions, as well as the gravity direction. In the method, an estimator initialization algorithm is implemented first to estimate the moving velocity and 3D positions of the feature points, and the gravity direction. Then, a recursive moving object detection algorithm is designed to classify the static and dynamic features based on feature re-projection. The simulation results show that the moving features can be grouped effectively, and the remaining static feature points can be used for camera pose and velocity estimation in a real scale to the ground.","PeriodicalId":186492,"journal":{"name":"2013 CACS International Automatic Control Conference (CACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128839692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Compliance control of wearable robotic fingers for rehabilitation applications","authors":"K. Song, Yea-Yen Chai","doi":"10.1109/CACS.2013.6734151","DOIUrl":"https://doi.org/10.1109/CACS.2013.6734151","url":null,"abstract":"The rehabilitation quality of stroke patients has drawn much attention in recent years. Researchers have developed various kinds of devices for lower and upper limb rehabilitation functions. In this paper, we present a 3 degrees-of-freedom (3-DOF) wearable robotic fingers for rehabilitation of task-oriented training in grasping tasks. A control strategy is proposed for grasping task training by considering user comfort based-on his/her intention as well as safety in task training procedures. The control strategy is divided into two parts: user compliance control and grasping compliance control. In user compliance control, we employed a mass-spring-damper model for grasping operation when the user exerts an intention force. This strategy allows the user to feel a comfortable guidance of the finger movement. In grasping compliance control, a similar model is used when the finger exoskeleton comes into contact with the object, that the system will become aware of the situation immediately and assist an appropriate grasping force for stable grasping. Experimental verification shows that the developed wearable rehabilitation robotic fingers can provide a comfortable fit for the testers and is capable to assist the testers to achieve grasping task training.","PeriodicalId":186492,"journal":{"name":"2013 CACS International Automatic Control Conference (CACS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123487178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}