Active Camera Relocalization with RGBD Camera from a Single 2D Image

Dongxu Miao, Fei-Peng Tian, Wei Feng

Abstract: Active camera relocalization (ACR) focuses on dynamically and physically relocating camera to a previous pose, effectively supporting many applications in computer vision and robotics, such as automated picking and stowing, fine-grained change detection. Previous work [1] uses barely 2D images to realize ACR, bringing about unknown translation scale problem. To solve this problem, they use bisection approach to guess translation scale, which leads to reciprocating motion and slows down the convergence process. In this paper, we utilize additional depth information from an RGBD camera to solve the real translation scale problem. Via iteratively and sequentially adjusting 3D translation and rotation, our ACR approach greatly reduces the iteration number and speeding up the process. To cope with imprecise pose estimation and achieve high relocalization accuracy, we propose a bounding strategy to restrict camera motion. Experiments validate the proposed method is much efficient and its accuracy is on par with previous ACR method.