In this article, we have investigated a VR simulator of a forestry crane used for loading logs onto a truck. We have mainly studied the Quality of Experience (QoE) aspects that may be relevant for task completion, and whether there are any discomfort related symptoms experienced during the task execution. QoE experiments were designed to capture the general subjective experience of using the simulator, and to study task performance. The focus was to study the effects of latency on the subjective experience, with regards to delays in the crane control interface. Subjective studies were performed with controlled delays added to the display update and hand controller (joystick) signals. The added delays ranged from 0 to 30 ms for the display update, and from 0 to 800 ms for the hand controller. We found a strong effect on latency in the display update and a significant negative effect for 800 ms added delay on latency in the hand controller (in total approx. 880 ms latency including the system delay). The Simulator Sickness Questionnaire (SSQ) gave significantly higher scores after the experiment compared to before the experiment, but a majority of the participants reported experiencing only minor symptoms. Some test subjects ceased the test before finishing due to their symptoms, particularly due to the added latency in the display update.

In this study, we investigate a VR simulator of a forestry crane used for loading logs onto a truck, mainly looking at Quality of Experience (QoE) aspects that may be relevant for task completion, but also whether there are any discomfort related symptoms experienced during task execution. A QoE test has been designed to capture both the general subjective experience of using the simulator and to study task performance. Moreover, a specific focus has been to study the effects of latency on the subjective experience, with regards to delays in the crane control interface. A formal subjective study has been performed where we have added controlled delays to the hand controller (joystick) signals. The added delays ranged from 0 ms to 800 ms. We found no significant effects of delays on the task performance on any scales up to 200 ms. A significant negative effect was found for 800 ms added delay. The Symptoms reported in the Simulator Sickness Questionnaire (SSQ) was significantly higher for all the symptom groups, but a majority of the participants reported only slight symptoms. Two out of thirty test persons stopped the test before finishing due to their symptoms.

In this paper, we investigate how different viewing positions affect a user's Quality of Experience (QoE) and performance in an immersive telepresence system. A QoE experiment has been conducted with 27 participants to assess the general subjective experience and the performance of remotely operating a toy excavator. Two view positions have been tested, an overhead and a ground-level view, respectively, which encourage reliance on stereoscopic depth cues to different extents for accurate operation. Results demonstrate a significant difference between ground and overhead views: the ground view increased the perceived difficulty of the task, whereas the overhead view increased the perceived accomplishment as well as the objective performance of the task. The perceived helpfulness of the overhead view was also significant according to the participants.

Virtual and augmented reality is increasingly prevalent in industrial applications, such as remote control of industrial machinery, due to recent advances in head-mounted display technologies and low-latency communications via 5G. However, the influence of augmentations and camera placement-based viewing positions on operator performance in telepresence systems remains unknown. In this paper, we investigate the joint effects of depth-aiding augmentations and viewing positions on the quality of experience for operators in augmented telepresence systems. A study was conducted with 27 non-expert participants using a real-time augmented telepresence system to perform a remote-controlled navigation and positioning task, with varied depth-aiding augmentations and viewing positions. The resulting quality of experience was analyzed via Likert opinion scales, task performance measurements, and simulator sickness evaluation. Results suggest that reducing the reliance on stereoscopic depth perception via camera placement has a significant benefit to operator performance and quality of experience. Conversely, the depth-aiding augmentations can partly mitigate the negative effects of inferior viewing positions. However the viewing-position based monoscopic and stereoscopic depth cues tend to dominate over cues based on augmentations. There is also a discrepancy between the participants' subjective opinions on augmentation helpfulness, and its observed effects on positioning task performance.

Remote operation of diggers, scalers, and other tunnel-boring machines has significant benefits for worker safety in underground mining. Real-time augmentation of the presented remote views can further improve the operator effectiveness through a more complete presentation of relevant sections of the remote location. In safety-critical applications, such augmentation cannot depend on preconditioned data, nor generate plausible-looking yet inaccurate sections of the view. In this paper, we present a capture and rendering pipeline for real time view augmentation and novel view synthesis that depends only on the inbound data from lidar and camera sensors. We suggest an on-the-fly lidar filtering for reducing point oscillation at no performance cost, and a full rendering process based on lidar depth upscaling and in-view occluder removal from the presented scene. Performance assessments show that the proposed solution is feasible for real-time applications, where per-frame processing fits within the constraints set by the inbound sensor data and within framerate tolerances for enabling effective remote operation.

Camera calibration methods are commonly evaluated on cumulative reprojection error metrics, on disparate one-dimensional datasets. To evaluate calibration of cameras in two-dimensional arrays, assessments need to be made on two-dimensional datasets with constraints on camera parameters. In this study, accuracy of several multi-camera calibration methods has been evaluated on camera parameters that are affecting view projection the most. As input data, we used a 15-viewpoint two-dimensional dataset with intrinsic and extrinsic parameter constraints and extrinsic ground truth. The assessment showed that self-calibration methods using structure-from-motion reach equal intrinsic and extrinsic parameter estimation accuracy with standard checkerboard calibration algorithm, and surpass a well-known self-calibration toolbox, BlueCCal. These results show that self-calibration is a viable approach to calibrating two-dimensional camera arrays, but improvements to state-of-art multi-camera feature matching are necessary to make BlueCCal as accurate as other self-calibration methods for two-dimensional camera arrays.

Recording and imaging the 3D world has led to the use of light fields. Capturing, distributing and presenting light field data is challenging, and requires an evaluation platform. We define a framework for real-time processing, and present the design and implementation of a light field evaluation system. In order to serve as a testbed, the system is designed to be flexible, scalable, and able to model various end-to-end light field systems. This flexibility is achieved by encapsulating processes and devices in discrete framework systems. The modular capture system supports multiple camera types, general-purpose data processing, and streaming to network interfaces. The cloud system allows for parallel transcoding and distribution of streams. The presentation system encapsulates rendering and display specifics. The real-time ability was tested in a latency measurement; the capture and presentation systems process and stream frames within a 40 ms limit.

Accurately recording motion from multiple perspectives is relevant for recording and processing immersive multi-media and virtual reality content. However, synchronization errors between multiple cameras limit the precision of scene depth reconstruction and rendering. In order to quantify this limit, a relation between camera de-synchronization, camera parameters, and scene element motion has to be identified. In this paper, a parametric ray model describing depth uncertainty is derived and adapted for the pinhole camera model. A two-camera scenario is simulated to investigate the model behavior and how camera synchronization delay, scene element speed, and camera positions affect the system's depth uncertainty. Results reveal a linear relation between synchronization error, element speed, and depth uncertainty. View convergence is shown to affect mean depth uncertainty up to a factor of 10. Results also show that depth uncertainty must be assessed on the full set of camera rays instead of a central subset.