Efficient camera-based pose estimation for real-time applications

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.

Authors

• Elmar Mair
• Klaus H. Strobl
• Michael Suppa
• Darius Burschka

Keywords

• Cameras
• Robustness
• Mobile robots
• Robot vision systems
• Intelligent robots
• Real time systems
• Layout
• Algorithm design and analysis
• Optical sensors
• Aerodynamics
• Pose Estimation
• Mobile Robot
• Mobile Platform
• Real-time Position
• Kalman Filter
• Singular Value Decomposition
• Inertial Measurement Unit
• Particle Filter
• Extended Kalman Filter
• Machine Vision
• Search Range
• Odometry
• Structure From Motion
• Root Mean Square Error Of Cross-validation
• Camera Pose
• Visual Localization
• Stereo Camera
• Real-time Capability
• Bundle Adjustment
• Stereo Matching
• Absolute Orientation
• Monocular Images
• Accurate Pose
• Tentative Model
• Reference Frame
• Field Of View
• Centroid
• Internal Model
• 3D Information