Optimization based IMU camera calibration

Inertia-visual sensor fusion has become popular due to the complementary characteristics of cameras and IMUs. Once the spatial and temporal alignment between the sensors is known, the fusion of measurements of these devices is straightforward. Determining the alignment, however, is a challenging problem. Especially the spatial translation estimation has turned out to be difficult, mainly due to limitations of camera dynamics and noisy accelerometer measurements. Up to now, filtering-based approaches for this calibration problem are largely prevalent. However, we are not convinced that calibration, as an offline step, is necessarily a filtering issue, and we explore the benefits of interpreting it as a batch-optimization problem. To this end, we show how to model the IMU-camera calibration problem in a nonlinear optimization framework by modeling the sensors' trajectory, and we present experiments comparing this approach to filtering and system identification techniques. The results are based both on simulated and real data, showing that our approach compares favorably to conventional methods.

Authors

• Michael Fleps
• Elmar Mair
• Oliver Ruepp
• Michael Suppa
• Darius Burschka

Keywords

• Cameras
• Vectors
• Spline
• Calibration
• Optimization
• Accelerometers
• Trajectory
• Inertial Measurement Unit
• Simulated Data
• Accelerometer
• Optimization Framework
• Spatial Alignment
• Calibration Problem
• Degrees Of Freedom
• Scaling Factor
• Angular Velocity
• Parameter Vector
• Kalman Filter
• Control Points
• Gyroscope
• Pose Estimation
• Coordinate Frame
• Basis Matrix
• Camera Frame
• Gray Box
• Gravity Vector
• Unscented Kalman Filter
• Relative Pose
• Camera Measurements
• Sequential Quadratic Programming
• Spatial Registration
• Root Mean Square Error Of Cross-validation
• First Approximation
• Objective Function
• 3D Coordinates
• Bias Values