  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.
