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

Victor Fragoso, Joseph DeGol, Gang Hua
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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 generalizedcamera-model pose-and-scale estimator that
more » ... tilizes 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.
doi:10.1109/cvpr42600.2020.00228 dblp:conf/cvpr/FragosoD020 fatcat:qawzmx63encn5hvsvqnrsy2r4u