Mid Sweden University

Remote operation and Augmented Telepresence are fields of interest for novel industrial applications in e.g., construction and mining. In this study, we report on an ongoing investigation of the Quality of Experience aspects of an Augmented Telepresence system for remote operation. The system can achieve view augmentation with selective content removal and Novel Perspective view generation. Two formal subjective studies have been performed with test participants scoring their experience while using the system with different levels of view augmentation. The participants also gave free-form feedback on the system and their experiences. The first experiment focused on the effects of in-view augmentations and interface distributions on wall patterns perception. The second one focused on the effects of augmentations on depth and 3D environment understanding. The participants’ feedback from experiment 1 showed that the majority of participants preferred to use the original camera views and the Disocclusion Augmentation view instead of Novel Perspective views. Moreover, the Disocclusion Augmentation, that was shown in combination with other views seemed beneficial. When the views were isolated in experiment 2, the impact of the Disocclusion Augmentation view was found to be lower than the Novel Perspective views.

 Observation and understanding of the world through digital sensors is an ever-increasing part of modern life. Systems of multiple sensors acting together have far-reaching applications in automation, entertainment, surveillance, remote machine control, and robotic self-navigation. Recent developments in digital camera, range sensor and immersive display technologies enable the combination of augmented reality and telepresence into Augmented Telepresence, which promises to enable more effective and immersive forms of interaction with remote environments.

The purpose of this work is to gain a more comprehensive understanding of how multi-sensor systems lead to Augmented Telepresence, and how Augmented Telepresence can be utilized for industry-related applications. On the one hand, the conducted research is focused on the technological aspects of multi-camera capture, rendering, and end-to-end systems that enable Augmented Telepresence. On the other hand, the research also considers the user experience aspects of Augmented Telepresence, to obtain a more comprehensive perspective on the application and design of Augmented Telepresence solutions.

This work addresses multi-sensor system design for Augmented Telepresence regarding four specific aspects ranging from sensor setup for effective capture to the rendering of outputs for Augmented Telepresence. More specifically, the following problems are investigated: 1) whether multi-camera calibration methods can reliably estimate the true camera parameters; 2) what the consequences are of synchronization errors in a multi-camera system; 3) how to design a scalable multi-camera system for low-latency, real-time applications; and 4) how to enable Augmented Telepresence from multi-sensor systems for mining, without prior data capture or conditioning. 

The first problem was solved by conducting a comparative assessment of widely available multi-camera calibration methods. A special dataset was recorded, enforcing known constraints on camera ground-truth parameters to use as a reference for calibration estimates. The second problem was addressed by introducing a depth uncertainty model that links the pinhole camera model and synchronization error to the geometric error in the 3D projections of recorded data. The third problem was addressed empirically - by constructing a multi-camera system based on off-the-shelf hardware and a modular software framework. The fourth problem was addressed by proposing a processing pipeline of an augmented remote operation system for augmented and novel view rendering.

The calibration assessment revealed that target-based and certain target-less calibration methods are relatively similar in their estimations of the true camera parameters, with one specific exception. For high-accuracy scenarios, even commonly used target-based calibration approaches are not sufficiently accurate with respect to the ground truth. The proposed depth uncertainty model was used to show that converged multi-camera arrays are less sensitive to synchronization errors. The mean depth uncertainty of a camera system correlates to the rendered result in depth-based reprojection as long as the camera calibration matrices are accurate. The presented multi-camera system demonstrates a flexible, de-centralized framework where data processing is possible in the camera, in the cloud, and on the data consumer's side. The multi-camera system is able to act as a capture testbed and as a component in end-to-end communication systems, because of the general-purpose computing and network connectivity support coupled with a segmented software framework. This system forms the foundation for the augmented remote operation system, which demonstrates the feasibility of real-time view generation by employing on-the-fly lidar de-noising and sparse depth upscaling for novel and augmented view synthesis.

In addition to the aforementioned technical investigations, this work also addresses the user experience impacts of Augmented Telepresence. The following two questions were investigated: 1) What is the impact of camera-based viewing position in Augmented Telepresence? 2) What is the impact of depth-aiding augmentations in Augmented Telepresence? Both are addressed through a quality of experience study with non-expert participants, using a custom Augmented Telepresence test system for a task-based experiment. The experiment design combines in-view augmentation, camera view selection, and stereoscopic augmented scene presentation via a head-mounted display to investigate both the independent factors and their joint interaction.

The results indicate that between the two factors, view position has a stronger influence on user experience. Task performance and quality of experience were significantly decreased by viewing positions that force users to rely on stereoscopic depth perception. However, position-assisting view augmentations can mitigate the negative effect of sub-optimal viewing positions; the extent of such mitigation is subject to the augmentation design and appearance.

In aggregate, the works presented in this dissertation cover a broad view of Augmented Telepresence. The individual solutions contribute general insights into Augmented Telepresence system design, complement gaps in the current discourse of specific areas, and provide tools for solving challenges found in enabling the capture, processing, and rendering in real-time-oriented end-to-end systems.

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.

Multi-camera systems are used in entertainment production, computer vision, industry and surveillance. The benefit of using multi-camera systems is the ability to recover the 3D structure, or depth, of the recorded scene. However, various types of cameras, including depth cameras, can not be reliably synchronized during recording, which leads to errors in depth estimation and scene rendering. The aim of this work is to propose a method for compensating synchronization errors in already recorded sequences, without changing the format of the recorded sequences. We describe a depth uncertainty model for parametrizing the impact of synchronization errors in a multi-camera system, and propose a method for synchronization error estimation and compensation. The proposed method is based on interpolating an image at a desired timeframe based on adjacent non-synchronized images in a single camera's sequence, using an array of per-pixel distortion vectors. This array is generated by using the difference between adjacent images to locate and segment the recorded moving objects, and does not require any object texture or distinguishing features beyond the observed difference in adjacent images. The proposed compensation method is compared with optical-flow based interpolation and sparse correspondence based morphing, and the proposed synchronization error estimation is compared with a state-of-the-art video alignment method. The proposed method shows better synchronization error estimation accuracy and compensation ability, especially in cases of low-texture, low-feature images. The effect of using data with synchronization errors is also demonstrated, as is the improvement gained by using compensated data. The compensation of synchronization errors is useful in scenarios where the recorded data is expected to be used by other processes that expect a sub-frame synchronization accuracy, such as depth-image-based rendering.

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.

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.

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.

The digital camera is the technological counterpart to the human eye, enabling the observation and recording of events in the natural world. Since modern life increasingly depends on digital systems, cameras and especially multiple-camera systems are being widely used in applications that affect our society, ranging from multimedia production and surveillance to self-driving robot localization. The rising interest in multi-camera systems is mirrored by the rising activity in Light Field research, where multi-camera systems are used to capture Light Fields - the angular and spatial information about light rays within a 3D space. 

The purpose of this work is to gain a more comprehensive understanding of how cameras collaborate and produce consistent data as a multi-camera system, and to build a multi-camera Light Field evaluation system. This work addresses three problems related to the process of multi-camera capture: first, whether multi-camera calibration methods can reliably estimate the true camera parameters; second, what are the consequences of synchronization errors in a multi-camera system; and third, how to ensure data consistency in a multi-camera system that records data with synchronization errors. Furthermore, this work addresses the problem of designing a flexible multi-camera system that can serve as a Light Field capture testbed.

The first problem is solved by conducting a comparative assessment of widely available multi-camera calibration methods. A special dataset is recorded, giving known constraints on camera ground-truth parameters to use as reference for calibration estimates. The second problem is addressed by introducing a depth uncertainty model that links the pinhole camera model and synchronization error to the geometric error in the 3D projections of recorded data. The third problem is solved for the color-and-depth multi-camera scenario, by using a proposed estimation of the depth camera synchronization error and correction of the recorded depth maps via tensor-based interpolation. The problem of designing a Light Field capture testbed is addressed empirically, by constructing and presenting a multi-camera system based on off-the-shelf hardware and a modular software framework.

The calibration assessment reveals that target-based and certain target-less calibration methods are relatively similar at estimating the true camera parameters. The results imply that for general-purpose multi-camera systems, target-less calibration is an acceptable choice. For high-accuracy scenarios, even commonly used target-based calibration approaches are insufficiently accurate. The proposed depth uncertainty model is used to show that converged multi-camera arrays are less sensitive to synchronization errors. The mean depth uncertainty of a camera system correlates to the rendered result in depth-based reprojection, as long as the camera calibration matrices are accurate. The proposed depthmap synchronization method is used to produce a consistent, synchronized color-and-depth dataset for unsynchronized recordings without altering the depthmap properties. Therefore, the method serves as a compatibility layer between unsynchronized multi-camera systems and applications that require synchronized color-and-depth data. Finally, the presented multi-camera system demonstrates a flexible, de-centralized framework where data processing is possible in the camera, in the cloud, and on the data consumer's side. The multi-camera system is able to act as a Light Field capture testbed and as a component in Light Field communication systems, because of the general-purpose computing and network connectivity support for each sensor, small sensor size, flexible mounts, hardware and software synchronization, and a segmented software framework.