The chip inside your phone contains multiple components, each supporting a specific function, such as image processing, graphic processing, and location. Galileo is not an application that you download; Galileo is a native feature of the smartphone itself. Hint : Not sure if your phone receives Galileo signals? We recommend downloading the GPSTest app to find out. Although some chips only track GPS or Glonass signals, more and more are including Galileo in the mix. Qualcomm and Mediatek. The best phone in this price segment. It's my second Meizu Phone. Performance is very smooth with it's Flyme and Android combination.
Mohd Alam Certified Buyer 5 months ago.
Pros Best value for money as I bought it for just performance is decent ok for normal use display is good battery backup is decent. Unless u play ga Dheepak Gunasekaran Certified Buyer 1 month ago. This is the best budget mobile under which I've orderd for my friend. Bharath Kommalapati Certified Buyer 4 months ago.
Feb Abm Musa Jakob Eriksson. While the satellite-based Global Positioning System GPS is adequate for some outdoor applications, many other applications are held back by its multi-meter positioning errors and poor indoor coverage. In this paper, we study the feasibility of real-time video-based localization on resource-constrained platforms. Before commencing a localization task, a video-based localization system downloads an offline model of a restricted target environment, such as a set of city streets, or an indoor shopping mall.
The system is then able to localize the user within the model, using only video as input. To enable such a system to run on resource-constrained embedded systems or smartphones, we a propose techniques for efficiently building a 3D model of a surveyed path, through frame selection and efficient feature matching, b substantially reduce model size by multiple compression techniques, without sacrificing localization accuracy, c propose efficient and concurrent techniques for feature extraction and matching to enable online localization, d propose a method with interleaved feature matching and optical flow based tracking to reduce the feature extraction and matching time in online localization.
Based on an extensive set of both indoor and outdoor videos, manually annotated with location ground truth, we demonstrate that sub-meter accuracy, at real-time rates, is achievable on smart-phone type platforms, despite challenging video conditions. Dec Imeen Ben salah. Visual Localization and Camera Pose Estimation.
Recent progress in image-based localization techniques have led to methods that are robust to changes in scene appearance and illumination [7,57], scalable [36,53,54,79], and efficient [9, 15,18,28,30,69]. Most localization approaches first recover putative matches between query image features and features associated with 3D structure.
This raises significant privacy concerns when consumers use such services in their homes or in confidential industrial settings. Even if only image features are uploaded, the privacy concerns remain as the images can be reconstructed fairly well from feature locations and descriptors. We propose to conceal the content of the query images from an adversary on the server or a man-in-the-middle intruder. The key insight is to replace the 2D image feature points in the query image with randomly oriented 2D lines passing through their original 2D positions.
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It will be shown that this feature representation hides the image contents, and thereby protects user privacy, yet still provides sufficient geometric constraints to enable robust and accurate 6-DOF camera pose estimation from feature correspondences. Our proposed method can handle single-and multi-image queries as well as exploit additional information about known structure, gravity, and scale.
Numerous experiments demonstrate the high practical relevance of our approach. Given a query image, the goal of visual localization problem is to estimate its camera pose, i.
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Aug Visual localization is the problem of estimating a camera within a scene and a key component in computer vision applications such as self-driving cars and Mixed Reality. State-of-the-art approaches for accurate visual localization use scene-specific representations, resulting in the overhead of constructing these models when applying the techniques to new scenes.
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Recently, deep learning-based approaches based on relative pose estimation have been proposed, carrying the promise of easily adapting to new scenes. However, it has been shown such approaches are currently significantly less accurate than state-of-the-art approaches.
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In this paper, we are interested in analyzing this behavior. To this end, we propose a novel framework for visual localization from relative poses. Using a classical feature-based approach within this framework, we show state-of-the-art performance. Replacing the classical approach with learned alternatives at various levels, we then identify the reasons for why deep learned approaches do not perform well.
Based on our analysis, we make recommendations for future work. Place recognition techniques are also related to the visual localization problem as they can be used to determine which part of a scene might be visible in a query image Cao and Snavely ;Sattler et al. As such, place recognition techniques are used to reduce the amount of data that has to be kept in RAM, as the regions visible in the retrieved images might be loaded from disk on demand Arth et al. Yet, loading 3D points from disk results in high query latency.
Large-scale, real-time visual-inertial localization revisited. Jun The overarching goals in image-based localization are scale, robustness and speed. In recent years, approaches based on local features and sparse 3D point-cloud models have both dominated the benchmarks and seen successful realworld deployment.
They enable applications ranging from robot navigation, autonomous driving, virtual and augmented reality to device geo-localization. Recently end-to-end learned localization approaches have been proposed which show promising results on small scale datasets. We aim to deploy localization at global-scale where one thus relies on methods using local features and sparse 3D models. Our approach spans from offline model building to real-time client-side pose fusion.
The system compresses appearance and geometry of the scene for efficient model storage and lookup leading to scalability beyond what what has been previously demonstrated. It allows for low-latency localization queries and efficient fusion run in real-time on mobile platforms by combining server-side localization with real-time visual-inertial-based camera pose tracking.
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In order to further improve efficiency we leverage a combination of priors, nearest neighbor search, geometric match culling and a cascaded pose candidate refinement step. This combination outperforms previous approaches when working with large scale models and allows deployment at unprecedented scale. We demonstrate the effectiveness of our approach on a proof-of-concept system localizing 2.