Graph based INS-camera calibration

Transcription

1 Graph based INS-camera calibration D. Bender a,b, M. Schikora a,b, J. Sturm b, D. Cremers b a Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany b Computer Vision Group, Technical University of Munich, Garching, Germany Fraunhofer FKIE

2 Classical Aero Triangulation Fraunhofer FKIE

3 Direct Georeferencing

4 Inertial Navigation System (INS)

5 Inertial Navigation System (INS)

6 Real Time Kinematic (RTK) correction signal 2 cm

7 Coordinate Systems

8 Coordinate Systems

9 Coordinate Systems

10 Rigid body transformation

11 Rigid body transformation lever-arm boresight

12 Rigid body transformation Fraunhofer FKIE

13 Graph optimization Aim of optimization: Find the configuration of parameters (nodes), which maximally explain the set of measurements (edges) Method: g 2 o (General Graph Optimization Framework) [1] C++ framework for solving least square problems Open Source Easily extendable [1] Kümmerle, R. and Grisetti, G., g2o: A General Framework for Graph Optimization. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp

14 Graph based INS-camera calibration Camera poses Pixel measurements 3D points

15 Graph based INS-camera calibration Camera poses Pixel measurements Intrinsic camera calibration 3D points

16 Graph based INS-camera calibration GPS/INS measurements Camera poses Pixel measurements Intrinsic camera calibration 3D points

17 Graph based INS-camera calibration INS-camera calibration GPS/INS measurements Camera poses Pixel measurements Intrinsic camera calibration 3D points

18 Simulated scenario

19 Monte Carlo simulation results RMSE lever-arm RMSE boresight 3,5 3, ,5 2, ,5 1, ,5 0,5 0 x [cm] y [cm] z [cm] 0 yaw [ ] pitch [ ] roll [ ] Initialization 1000 points 3000 points 6000 points Initialization 1000 points 3000 points 6000 points

20 Monte Carlo simulation results RMSE lever-arm RMSE boresight 3,5 0,02 3 2,5 0, ,5 0,01 1 0,5 0,005 0 x [cm] y [cm] z [cm] 0 yaw [ ] pitch [ ] roll [ ] Initialization 1000 points 3000 points 6000 points 1000 points 3000 points 6000 points RMSE intrinsic camera calibration f_x f f_y [pel] o_x o_y k1 [pel^2] k2 [pel^4] x [pel] f y [pel] o x [pel] o y [pel] k 1 2 ] k 2 4 ] Initialization 1000 points 3000 points 6000 points

21 Monte Carlo simulation results RMSE lever-arm RMSE boresight 3,5 3 2,5 0,02 0, ,5 0,01 1 0,5 0,005 0 x [cm] y [cm] z [cm] 0 yaw [ ] pitch [ ] roll [ ] Initialization 1000 points 3000 points 6000 points RMSE intrinsic camera calibration 1000 points 3000 points 6000 points 2,5 2 1,5 1 0,5 0 ff_x [pel] f_y [pel] o_x o_y k1 [pel^2] k2 [pel^4] x [pel] f y [pel] o x [pel] o y [pel] k 1 2 ] k 2 4 ] 1000 points 3000 points 6000 points

22 Monte Carlo simulation results RMSE boresight 0,03 0,025 0,02 0,015 0,01 0,005 0 yaw [ ] pitch [ ] roll [ ] Stripes Square Star Stripes (high altitude) RMSE intrinsic camera calibration 1,2 1 0,8 0,6 0,4 0,2 0 f_x f x [pel] f_y f y [pel] o_x o x [pel] o_y o y [pel] k1 k[pel^2] 1 2 ] k2 k[pel^4] 2 4 ] Stripes Square Star Stripes (high altitude)

23 Summary and future work Summary Aim: Direct georeferencing of camera images with high precision INS Method: Determination of the offsets between INS and camera with a graph based bundle adjustment High accuracies for angle misalignment and intrinsic camera parameters Future work: Test with real data Test of the needed accuracies of the INS