Sensor Fusion for Augmented Reality

Caarls, J Jurjen; Jonker, Pieter; Persa, Stelian

doi:10.1007/978-3-540-39863-9_13

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…Gilson et al [10] performed a quantitative analysis of tracking systems and found that tracking performance deteriorates when the tracked ob- ject's speed increases. Others have attempted to estimate and fix these errors, such as Caarls et al [6] who presents a framework for leveraging multiple sensors simultaneously to achieve a more precise and robust tracking system, providing 1 cm z-accuracy for distances up to 120 cm and small roll errors at distances under 70 cm.…”

Section: Related Workmentioning

confidence: 99%

How wrong can you be: Perception of static orientation errors in mixed reality

Madsen

Stenholt

2014

2014 IEEE Symposium on 3D User Interfaces (3DUI)

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Tracking technologies are becoming an affordable commodity due to the wide use in mobile devices today. However, all tracking technologies available in commodity hardware is error prone due to problems such as drift, latency and jitter. The current understanding of human perception of static tracking errors is limited. This information about human perception might be useful in designing tracking systems for the display of AR and VR scenarios on commodity hardware. In this paper we present the findings of a study on the human perception of static orientation errors in a tracking system, using two different setups leveraging a handheld viewfinder: a classical augmented scenario and an indirect augmented one. By categorizing static orientation errors by scenario and local orientation axis, new insights into the users' ability to register orientational errors in the system are found. Our results show that users are much more aware of errors in classical AR scenarios in comparison to indirect AR scenarios. For both scenarios, the users registered roll orientation errors differently from both pitch and yaw orientation errors, and pitch and yaw perception is highly dependent on the scenario. However, the users performance ranking for orientational errors in AR scenarios was unexpected.

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Section: Related Workmentioning

confidence: 99%

How wrong can you be: Perception of static orientation errors in mixed reality

Madsen

Stenholt

2014

2014 IEEE Symposium on 3D User Interfaces (3DUI)

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“…Therefore, this method is not optimal if no reference nodes are available along the trajectory. The authors of [ 19 , 20 , 21 ] use a combination of inertial sensors and a camera for motion tracking in augmented reality applications. Most fusion sensor works use inertial sensors to aid computer vision methods, making the system more accurate and more robust.…”

Section: Related Workmentioning

confidence: 99%

Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles

Audi

Pierrot-Deseilligny²,

Meynard

et al. 2017

Sensors

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Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles) exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l’information géographique) camera, which has an IMU (Inertial Measurement Unit) sensor and an SoC (System on Chip)/FPGA (Field-Programmable Gate Array). To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N-th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test) detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.

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“…Sensor fusion algorithms with Kalman filtering [17], [18] was used to smooth the combined position tracking with data inputs and produce more accurate result from ultrasonic and optical tracking. Through this approach, the 6 DOF robust and smooth tracking in real time can be achieved with high speed movements in our system.…”

Section: Robust Real-time Hybrid Trackingmentioning

confidence: 99%

An Immersive VR System for Sports Education

Song

Fong

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYThe development of new technologies has undoubtedly promoted the advances of modern education, among which Virtual Reality (VR) technologies have made the education more visually accessible for students. However, classroom education has been the focus of VR applications whereas not much research has been done in promoting sports education using VR technologies. In this paper, an immersive VR system is designed and implemented to create a more intuitive and visual way of teaching tennis. A scalable system architecture is proposed in addition to the hardware setup layout, which can be used for various immersive interactive applications such as architecture walkthroughs, military training simulations, other sports game simulations, interactive theaters, and telepresent exhibitions. Realistic interaction experience is achieved through accurate and robust hybrid tracking technology, while the virtual human opponent is animated in real time using shader-based skin deformation. Potential future extensions are also discussed to improve the teaching/learning experience.

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Sensor Fusion for Augmented Reality

Cited by 10 publications

References 3 publications

How wrong can you be: Perception of static orientation errors in mixed reality

How wrong can you be: Perception of static orientation errors in mixed reality

Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles

An Immersive VR System for Sports Education

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