gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors
This work addresses the need for efficient pose-and-scale estimation in applications like augmented reality and robotics, but it is incremental as it builds on existing generalized-camera-model estimators.
The paper tackles the problem of fast and accurate camera pose-and-scale estimation for AR, mapping, and robotics by introducing gDLS*, a method that uses rotation and scale priors to balance speed and accuracy. It shows that gDLS* accelerates estimation and improves scale and pose accuracy compared to state-of-the-art methods like gDLS in experiments on synthetic and real data.
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.