Micro aerial vehicles (MAVs) are gaining importance as image acquisition tools in urban environments, where areas of interest are often close to buildings and to the ground. While GPS is still the most widely used sen...
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ISBN:
(纸本)9780769548739
Micro aerial vehicles (MAVs) are gaining importance as image acquisition tools in urban environments, where areas of interest are often close to buildings and to the ground. While GPS is still the most widely used sensor for outdoor localization, urban applications motivate the change towards visual localization. We present a framework based on metric, geo-referenced visual landmarks, which can be obtained by taking images with a consumer camera at ground level. Visual landmarks serve as prior knowledge to the MAV and allow robust, high-accuracy localization in urban environments. The issue of differing camera views in higher altitudes is reduced by incremental feature updates, a novel technique which boosts the performance by 30% in comparison to previous work, facilitates long-term operation, and results in a localization rate of 83%. We validate the visual pose estimation in-flight by comparison to IMU and GPS data, and evaluate our positioning accuracy with respect to differential GPS.
A novel user interface concept for camera phones, called "Hyperlinking Reality via Camera Phones", that we present in this article, provides a solution to one of the main challenges facing mobile user interf...
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A novel user interface concept for camera phones, called "Hyperlinking Reality via Camera Phones", that we present in this article, provides a solution to one of the main challenges facing mobile user interfaces, that is, the problem of selection and visualization of actions that are relevant to the user in her current context. Instead of typing keywords on a small and inconvenient keypad of a mobile device, a user of our system just snaps a photo of her surroundings and objects in the image become hyperlinks to information. Our method commences by matching a query image to reference panoramas depicting the same scene that were collected and annotated with information beforehand. Once the query image is related to the reference panoramas, we transfer the relevant information from the reference panoramas to the query image. By visualizing the information on the query image and displaying it on the camera phone's (multi-)touch screen, the query image augmented with hyperlinks allows the user intuitive access to information.
In this paper we present a Disaster Invariant Feature (DIF), which is used for localization of Unmanned Aerial Vehicles (UAV). There exist numerous researches that address the problem of localization of UAVs using aer...
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ISBN:
(纸本)9781424466757
In this paper we present a Disaster Invariant Feature (DIF), which is used for localization of Unmanned Aerial Vehicles (UAV). There exist numerous researches that address the problem of localization of UAVs using aerial images. However, after a disaster such as a tornado or an earthquake many features in aerial images like monuments and unique buildings may change, and the image-based localization would become hard or even impossible. Consequently it is important to find features that remain unchanged or with fairly small changes, and can be detected both before and after a disaster. We have used a recent method for street detection from aerial images and shown that road networks and segments are disaster invariant and could be utilized for localization and mapping. The algorithm has been implemented and tested on satellite images from Google, with nearly equivalent resolution to aerial images. The successful result of detecting this DIF on Port-au-Prince, in Haiti, images before and after the recent earthquake is presented.
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