论文标题
GDLS*：给定量表和重力先验的广义姿势和尺度估计
gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors
论文作者
论文摘要
在增强现实（AR），3D映射和机器人技术中，许多现实世界应用都需要快速准确地估算来自多个相机或单个移动相机捕获的多个图像的相机姿势和尺度。在姿势和规模的估计器中实现高速并保持高精度通常是矛盾的目标。为了同时实现这两个方面，我们利用有关解决方案空间的先验知识。我们提出了使用旋转和规模先验的广义相机模型姿势和尺度估计器GDLS*。 GDLS*允许应用程序灵活权衡每个先前的贡献，这很重要，因为先验通常来自嘈杂的传感器。与最先进的广义置换估计量（例如GDL）相比，我们对合成数据和真实数据的实验始终表明GDL*加速了估计过程并提高了规模和姿势准确性。
Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera. Achieving high speed and maintaining high accuracy in a pose-and-scale estimator are often conflicting goals. To simultaneously achieve both, we exploit a priori knowledge about the solution space. We present gDLS*, a generalized-camera-model pose-and-scale estimator that utilizes rotation and scale priors. gDLS* allows an application to flexibly weigh the contribution of each prior, which is important since priors often come from noisy sensors. Compared to state-of-the-art generalized-pose-and-scale estimators (e.g., gDLS), our experiments on both synthetic and real data consistently demonstrate that gDLS* accelerates the estimation process and improves scale and pose accuracy.
