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Many real-world applications in augmented reality (AR), 3D mapping, androbotics require both fast and accurate estimation of camera poses and scalesfrom multiple images captured by multiple cameras or a single moving camera.Achieving high speed and maintaining high accuracy in a pose-and-scaleestimator are often conflicting goals. To simultaneously achieve both, weexploit a priori knowledge about the solution space. We present gDLS*, ageneralized-camera-model pose-and-scale estimator that utilizes rotation andscale priors. gDLS* allows an application to flexibly weigh the contribution ofeach 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 demonstratethat gDLS* accelerates the estimation process and improves scale and poseaccuracy.

gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors