Latency has always been one of the essential problems within Virtual Reality (VR) domain since VR is inherently an interactive paradigm which performs the real-time estimation of human motions. From the user's point of view, the latency extremely reduces the presence experience of VR systems, especially when user won’t able to perform interactions accurately. To compensate the excessive latency, different prediction methods on human motion were studied in recent years. Among them, Kalman Filter was the most popular choice. However, the effectiveness of using Kalman Filter to eliminate the latency for VR systems is not always satisfactory in practice since the accuracy of the estimation of the users’ motion depends on several factors: the linearity of the motion, the prediction time, the computational time, and the algorithm’s limitation.Therefore, this thesis presents a VR-based haptic teleoperation system to study how to effectively eliminate the latency effectively using Kalman Filter. For investigating the performances of different prediction methods for VR systems with several factors considered, two types of Kalman Filter: Linear Kalman Filter (LKF) and Unscented Kalman Filter (UKF) have been used to predict the haptic motion dataset, under different amount of simulated latencies.The result shows, both LKF and UKF provide a good performance at compensating the latency. For 200ms latency, both filters satisfactorily eliminate the latency and improve the interaction effectiveness. The comparative study shows, LKF provides better performance since the linear rotational motion dataset captured by haptic device was used; both filters show a reduced performance when the prediction time is increased. Besides, UKF requires more computational time than LKF.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-189139 |
| Date | January 2016 |
| Creators | XuXiao, Ma |
| Publisher | KTH, Skolan för datavetenskap och kommunikation (CSC) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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