Multicamera System for Automatic Positioning of Objects in Game Sports

Garantir um sistema com múltiplas câmaras que seja capaz de extrair dados 3D da posição de uma bola durante um evento desportivo, através da análise e teste de técnicas de visão computacional (calibração de câmaras e reconstrução 3D)

Decoupled Multicamera Sensing for Flexible View Generation

10.1155/2016/8137859Journal of Sensors2016813785

Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community

Virtual reality systems for rodents

Over the last decade virtual reality ( VR) setups for rodents have been developed and utilized to investigate the neural foundations of behavior. Such VR systems became very popular since they allow the use of state-of-the-art techniques to measure neural activity in behaving rodents that cannot be easily used with classical behavior setups. Here, we provide an overview of rodent VR technologies and review recent results from related research. We discuss commonalities and differences as well as merits and issues of different approaches. A special focus is given to experimental ( behavioral) paradigms in use. Finally we comment on possible use cases that may further exploit the potential of VR in rodent research and hence inspire future studies

Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions presented in this thesis. To deliver the portability goal with a single off-the-shelf camera, we have taken two approaches: The first one, and the most extensively studied here, revolves around an unorthodox camera-mirrors configuration (catadioptrics) achieving a stereo omnidirectional system (SOS). The second approach relies on expanding the visual features from the scene into higher dimensionalities to track the pose of a conventional camera in a photogrammetric fashion. The first goal has many interdependent challenges, which we address as part of this thesis: SOS design, projection model, adequate calibration procedure, and application to VO. We show several practical advantages for the single-camera SOS due to its complete 360-degree stereo views, that other conventional 3D sensors lack due to their limited field of view. Since our omnidirectional stereo (omnistereo) views are captured by a single camera, a truly instantaneous pair of panoramic images is possible for 3D perception tasks. Finally, we address the VO problem as a direct multichannel tracking approach, which increases the pose estimation accuracy of the baseline method (i.e., using only grayscale or color information) under the photometric error minimization as the heart of the “direct” tracking algorithm. Currently, this solution has been tested on standard monocular cameras, but it could also be applied to an SOS. We believe the challenges that we attempted to solve have not been considered previously with the level of detail needed for successfully performing VO with a single camera as the ultimate goal in both real-life and simulated scenes

An investigation into web-based panoramic video virtual reality with reference to the virtual zoo.

Panoramic image Virtual Reality (VR) is a 360 degree image which has been interpreted as a kind of VR that allows users to navigate, view, hear and have remote access to a virtual environment. Panoramic Video VR builds on this, where filming is done in the real world to create a highly dynamic and immersive environment. This is proving to be a very attractive technology and has introduced many possible applications but still present a number of challenges, considered in this research. An initial literature survey identified limitations in panoramic video to date: these were the technology (e.g. filming and stitching) and the design of effective navigation methods. In particular, there is a tendency for users to become disoriented during way-finding. In addition, an effective interface design to embed contextual information is required. The research identified the need to have a controllable test environment in order to evaluate the production of the video and the optimal way of presenting and navigating within the scene. Computer Graphics (CG) simulation scenes were developed to establish a method of capturing, editing and stitching the video under controlled conditions. In addition, a novel navigation method, named the “image channel” was proposed and integrated within this environment. This replaced hotspots: the traditional navigational jumps between locations. Initial user testing indicated that the production was appropriate and did significantly improve user perception of position and orientation over jump-based navigation. The interface design combined with the environment view alone was sufficient for users to understand their location without the need to augment the view with an on screen map. After obtaining optimal methods in building and improving the technology, the research looked for a natural, complex, and dynamic real environment for testing. The web-based virtual zoo (World Association of Zoos and Aquariums) was selected as an ideal production: It had the purpose to allow people to get close to animals in their natural habitat and created particular interest to develop a system for knowledge delivery, raising protection concerns, and entertaining visitors: all key roles of a zoo. The design method established from CG was then used to develop a film rig and production unit for filming a real animal habitat: the Formosan rock monkey in Taiwan. A web-based panoramic video of this was built and tested though user experience testing and expert interviews. The results of this were essentially identical to the testing done in the prototype environment, and validated the production. Also was successfully attracting users to the site repeatedly. The research has contributed to new knowledge in improvement to the production process, improvement to presentation and navigating within panoramic videos through the proposed Image Channel method, and has demonstrated that web-based virtual zoo can be improved to help address considerable pressure on animal extinction and animal habitat degradation that affect humans by using this technology. Further studies were addressed. The research was sponsored by Taiwan’s Government and Twycross Zoo UK was a collaborator

REAL-TIME CAPTURE AND RENDERING OF PHYSICAL SCENE WITH AN EFFICIENTLY CALIBRATED RGB-D CAMERA NETWORK

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. With the recent explosive growth of Augmented Reality (AR) and Virtual Reality (VR) platforms, utilizing camera RGB-D camera networks to capture and render dynamic physical space can enhance immersive experiences for users. To maximize coverage and minimize costs, practical applications often use a small number of RGB-D cameras and sparsely place them around the environment for data capturing. While sparse color camera networks have been studied for decades, the problems of extrinsic calibration of and rendering with sparse RGB-D camera networks are less well understood. Extrinsic calibration is difficult because of inappropriate RGB-D camera models and lack of shared scene features. Due to the significant camera noise and sparse coverage of the scene, the quality of rendering 3D point clouds is much lower compared with synthetic models. Adding virtual objects whose rendering depend on the physical environment such as those with reflective surfaces further complicate the rendering pipeline. In this dissertation, I propose novel solutions to tackle these challenges faced by RGB-D camera systems. First, I propose a novel extrinsic calibration algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Second, I propose a novel rendering pipeline that can capture and render, in real-time, dynamic scenes in the presence of arbitrary-shaped reflective virtual objects. Third, I have demonstrated a teleportation application that uses the proposed system to merge two geographically separated 3D captured scenes into the same reconstructed environment. To provide a fast and robust calibration for a sparse RGB-D camera network, first, the correspondences between different camera views are established by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic using rigid transformation that is optimal only for pinhole cameras, different view transformation functions including rigid transformation, polynomial transformation, and manifold regression are systematically tested to determine the most robust mapping that generalizes well to unseen data. Third, the celebrated bundle adjustment procedure is reformulated to minimize the global 3D projection error so as to fine-tune the initial estimates. To achieve a realistic mirror rendering, a robust eye detector is used to identify the viewer\u27s 3D location and render the reflective scene accordingly. The limited field of view obtained from a single camera is overcome by our calibrated RGB-D camera network system that is scalable to capture an arbitrarily large environment. The rendering is accomplished by raytracing light rays from the viewpoint to the scene reflected by the virtual curved surface. To the best of our knowledge, the proposed system is the first to render reflective dynamic scenes from real 3D data in large environments. Our scalable client-server architecture is computationally efficient - the calibration of a camera network system, including data capture, can be done in minutes using only commodity PCs