Absolute Spatial Context-aware visual feature descriptors for outdoor handheld camera localization overcoming visual repetitiveness in urban environments
Absolute Spatial Context-aware visual feature descriptors for outdoor handheld camera localization overcoming visual repetitiveness in urban environments
复制标题
用于户外手持摄像机定位的绝对空间上下文感知视觉特征描述符，克服城市环境中的视觉重复性
DOI：
10.5220/0004683300560067
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
G. Klinker
中科院分区：
文献类型：
--
作者：
Daniel Kurz;P. Meier;Alexander Plopski;G. Klinker
我们提出了一个框架，使6DoF摄像机定位在户外环境中提供视觉特征描述符与绝对空间上下文（ASPAC）。这些描述符将来自特征周围的图像块的视觉信息与空间信息相结合，基于环境模型和连接到相机的传感器的读数，例如GPS，加速度计和数字罗盘。其结果是对相机图像中的特征进行更清晰的描述，这些特征对应于环境中的3D点。这在包含大量重复视觉特征的城市环境中特别有用。此外，我们描述了第一个全面的测试数据库，用于室外手持摄像机定位，包括超过45,000个真实的摄像机图像的城市环境，在自然的摄像机运动和不同的照明设置下捕获。对于所有这些图像，数据集不仅包含连接到相机的传感器的读数，而且还包含关于完整6DoF相机姿态的地面实况信息，以及环境的几何和纹理。基于我们向公众提供的这个数据集，我们表明，与最先进的方法相比，使用我们提出的框架可以提供更快的匹配和更好的定位结果。
We present a framework that enables 6DoF camera localization in outdoor environments by providing visual feature descriptors with an Absolute Spatial Context (ASPAC). These descriptors combine visual information from the image patch around a feature with spatial information, based on a model of the environment and the readings of sensors attached to the camera, such as GPS, accelerometers, and a digital compass. The result is a more distinct description of features in the camera image, which correspond to 3D points in the environment. This is particularly helpful in urban environments containing large amounts of repetitive visual features. Additionally, we describe the first comprehensive test database for outdoor handheld camera localization comprising of over 45,000 real camera images of an urban environment, captured under natural camera motions and different illumination settings. For all these images, the dataset not only contains readings of the sensors attached to the camera, but also ground truth information on the full 6DoF camera pose, and the geometry and texture of the environment. Based on this dataset, which we have made available to the public, we show that using our proposed framework provides both faster matching and better localization results compared to state-of-the-art methods.
DOI：
10.1109/34.888718
发表时间：
2000-11-01
影响因子：
23.6
作者：
Zhang, ZY
通讯作者：
Zhang, ZY