Efficient Camera-Based Pose Estimation for Real-Time Applications

Abstract

Accurate online localization is crucial for mobile robotics. In this paper, we describe a real-time image-based localization technique, which is based on a single calibrated camera. This can be supported by a second camera to improve accuracy and to provide the correct translational scale. Our goal is a robust and unbiased pose estimation in highly dynamic scenes on resource-limited systems. The presented approach is characterized through significantly improved robustness of the pose estimation, a novel approach for stereo subpixel accurate landmark initialization, and the speed-up of conventional tracking routines to achieve online capability. Although the algorithm is designed for accurate, online short-range egomotion estimation in hand-held scanning devices, it can be used for any mobile robot application as shown in this paper. Various tests and experimental results with a mobile platform and a hand-held 3D modeler are presented and discussed.