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Sparse Surface Adjustment

Sparse Surface Adjustment. M. Ruhnke , R. Kümmerle, G. Grisetti, W. Burgard. Metric 3D Models. Essential for tasks like: Object recognition Manipulation. Metric 3D Models. Essential for tasks like: Object recognition Manipulation Key challenges in model acquisition with mobile robots

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Sparse Surface Adjustment

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  1. Sparse Surface Adjustment M. Ruhnke, R. Kümmerle, G. Grisetti, W. Burgard

  2. Metric 3D Models Essential for tasks like: Object recognition Manipulation

  3. Metric 3D Models Essential for tasks like: Object recognition Manipulation Key challenges in model acquisition with mobile robots Errors in pose estimate Measurement errors

  4. Model Creation Optimize the sensor poses Registration / SLAM Reduce the impact of measurement errors Use optimal sensor distance Local noise reduction techniques (Moving Least Squares, Statistical Outlier Removal, …) Pose information is mostly not considered Sensor pose gives information about normal direction and range of the measurement

  5. Sparse Surface Adjustment Goal: Jointly optimize robot poses and surface points positions Surface Model Model of measurement uncertainties Data association: find corresponding points Utilize sparse graph optimizer framework g2o

  6. Surface Model Range measurements sample surfaces Assumption: Piecewise regular surfaces Surface sample 3D Position covariance normal (local neighborhood)

  7. Range measurement Sensor specific Covariance Dependent on range and incidence angle Gaussian error distributions Sensor Model Kinect RGB-D side view front view ~ 3.5m ~ 0.7m

  8. Data Association Normal shooting as data association heuristic Assign surfaces samples of different observations Covariance Large error weight in direction of the normal Small weight for errors in tangential direction

  9. Optimization Iteratively: Optimize system with g2o Re-compute: surface point characteristics (covariance, normals) data association

  10. SSA 2D: Intel Dataset

  11. Object 5mm Resolution

  12. AASS Loop Dataset* SLAM result (input) SSA result *Courtesy of Martin Magnusson, AASS, Örebro, Sweden

  13. SSA 3D: Example Cup

  14. Example: Scan Refinement SSA refines scans based on more certain nearby measurements raw scan after optimization

  15. Comparison SSA / MLS Moving Least Squares (MLS) Local smoothing method No correction of robot poses Sparse Surface Adjustment (SSA) Robot pose correction & smoother surfaces MLS result SSA result

  16. Summary SSA Iterative refinement of Sensor poses Surface points positions Considering range & sensor dependent uncertainties Re-computation of data association Uses PCL and FLANN

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