Collaborative stereo

In this paper, we propose a method to recover the relative pose of two robots in absolute scale and in real-time using one monocular camera on each robot. We achieve this by fusing measurements from the onboard inertial sensors on each platform with information obtained from feature correspondences between the two cameras using an Extended Kalman Filter (EKF). This forms a flexible stereo rig, providing the ability to treat the two robots as one single dynamic sensor, which can adapt to the environment and thus improve environmental mapping, obstacle avoidance and navigation. We demonstrate the power of this approach on both simulation and real datasets, employing two micro aerial vehicles (MAVs) to illustrate successful operation over general 3D motion.

Authors

• Markus W. Achtelik
• Stephan Weiss 0002
• Margarita Chli
• Frank Dellaert
• Roland Siegwart

Keywords

• Vehicles
• Acceleration
• Cameras
• Robots
• Quaternions
• Mathematical model
• Estimation
• Corresponding Points
• Inertial Measurement Unit
• Obstacle Avoidance
• Extended Kalman Filter
• 3D Motion
• Absolute Scale
• Relative Pose
• Monocular Camera
• Micro Air Vehicles
• Covariance Matrix
• Data Visualization
• Accelerometer
• Visual Information
• Random Walk
• Angular Velocity
• Equation Of State
• Relative Orientation
• Data Fusion
• Linear Accelerator
• Pose Estimation
• Inertial Frame
• Yaw Angle
• Coordinate Frame
• Vehicle Acceleration
• Gravity Vector
• Vicon System
• Vehicle Position
• Local Coordinate System
• Onboard Computer
• Single Camera