1. Proximity Computations between Noisy Point Clouds using Robust Classification 1 Jia Pan, 2 Sachin Chitta, 1 Dinesh Manocha 1 UNC Chapel Hill 2 Willow Garage http://gamma.cs.unc.edu/POINTC/

2. Main Result Proximity computation and collision detection algorithm between noisy point cloud data Computes collision probability instead of simple yes-no result Important for safety and feasibility of robotics applications

3. Proximity and Collision Computations Geometric reasoning with noisy point cloud data vs. mesh based representations Integral part of motion planning and grasping algorithms Contact computations for dynamic simulation

4. Background

5. Motion Planning: Assumptions Motion planning has a long history (30+ years) Assumptions – Exact environment (mesh world) – Exact control – Exact localization – No joint limit and torque limit – No quality control Output: – Collision-free path

6. Environment Uncertainty Uncertainty can be large due to sensor error, poor sampling, physical representations, etc. Need to model order to improve the safety and robustness of motion planning Relative less work – Previous methods only consider 2D cases with specific assumptions on uncertainty

7. Sensors Robot uses sensors to compute a representation of the physical world But sensors are not perfect or may not be very accurate….

8. Robot Sensors: Data Collection Cameras: may have low resolution

9. Robot Sensors: Data Collection Laser Scanners: may have limited range

10. Point Clouds Captured by Robot

11. Recent Trend: Depth Cameras Primesense Pr2’s Camcube Swissranger 4000 Kinect

12. http://graphics.stanford.edu/~mdfisher/Kinect.html

13. Kinect Reconstruction http://groups.csail.mit.edu/rrg/index.php?n=Main.VisualOdometryForGPS-DeniedFlight http://www.cs.washington.edu/ai/Mobile_Robotics/projects/rgbd-3d-mapping/

14. Uncertainty From RGB-D Sensors Sensors may have low resolution  low resolution of point clouds – Kinect has relatively high resolution, but may still be not enough for objects far away Sensors may be influenced by noise, especially in outdoor environments  noise in point clouds Sensors may have limited ranges (near range and far range)  unknown areas

15. Handling Noisy Point Cloud Planning, navigation and grasping Scene reasoning Noisy data Real-time processing

16. Related Work Collision Detection for meshes – Fast and robust – Not designed for (noisy) point-clouds Motion planning with environment uncertainty – 2D polygons – Vehicle planning

17. Uncertainty Model of Point Cloud

18. Errors in Point Clouds Discretization (sampling) error

19. Errors in Point Clouds Position error

20. Point Cloud Collision Detection In-collision In-collision ?

21. Point Cloud Collision Detection In-collision In-collision ?

22. Point Cloud Collision Detection collision-free Collision-free ?

23. Point Cloud Collision Detection Collision-free Collision-free?

24. Handling Point Cloud Collision: Two Methods Reconstruction Mesh Collision Point Cloud Collision

25. Mesh Reconstruction => Collision Mesh CollisionReconstruction Reconstruction is more difficult then collision detection Solve an easier problem by conquering a more difficult one?

26. Mesh Reconstruction => Collision Reconstruction process is not robust, and is sensitive to noise and high order features Error in reconstructed result can be amplified by subsequent collision checking Reconstruction process is slow (few seconds) The final result is YES/NO answer, which is sensitive to noise.

27. Use Ideas from Classification Two methods to classify two sets (Machine Learning) – Generative model First estimate the joint distribution (more difficult!) Then compute the classifier – Discriminative function Directly compute the classifier If we only need to classify, discriminative function usually is the better choice.

28. Our Solution Return to the basic definition of collision-free – Two objects are collision-free if they are separable by a continuous surface and is in-collision when such surface does not exist.

29. Classification-based Collision Detection Find a separating surface that separates two points clouds as much as possible

30. Collision Detection based on Robust Classification We compute the optimal (i.e. minimize the separating error) separating surface using a SVM-like algorithm Use supervised machine learning methods for geometric classification Different from standard SVM: each training data point has noise – corresponds to robust classification in machine learning

31. Robust Classification Standard SVM Robust Classification: aware of noise

32. Compute the Separating Surface

33. Per-point Collision Probability Collision probability: the probability that one point is on the wrong side of separating surface. Robust classification computes collision probability for each single point sample

34. Probabilistic Collision between Two Objects For each object – Cluster the points and only keep one point in each cluster: compute collision probability for independent points – Overall object collision probability

35. Three Collision Cases Deep collision In-contactLarge distance Collision probability near 1 Collision probability near 0.5 Collision probability near 0 Difficult: small noise will bring reverse of yes/no answer Easy

36. Results: Small Noise Deep Collision Configurations Large Distance Configurations In Contact Configurations (Difficult) Very few configurations are in the difficult region

37. Results: Large Noise Deep Collision Configurations Large Distance Configurations In Contact Configurations (Difficult) More configurations are in the difficult region!

38. PR2 Robot Sensor Results Deep Collision Configurations In Contact Configurations (Difficult) Large Distance Configurations same distance to obstacle  collision probability with wide spread.

39. Range of Collision Probability Collision probability’s wide range: – It is a more complete description of collision state than distance or yes/no answer – Important for grasping or planning in constrained environment

40. Kinect Data Office data from Peter Henry, Dieter Fox @ RSE-lab UW

41. Result on Kinect Data

42. BVH Hierarchy Acceleration Bounding volume hierarchy is widely used in mesh collision detection for acceleration Basic idea: decompose objects into many cells and cull collision test between cells that are far away.

43. BVH Hierarchy for Point Clouds Similar to BVH hierarchy for mesh But has important difference – Leaf nodes should contain enough points for SVM classification – Bounding box for each cell should be expanded a bit to avoid miss collision

44. Reason for Expansion (a) BVH without overlap (b) BVH with overlap BV-1BV-2 BV-1 BV-2

45. BVH: pros and cons Much Faster But the collision probability for some points may be not correct (overall collision probability is not influenced) – Reason ： BVH loses global information and can not handle deep penetration points

46. Benefit Speed is comparable to mesh-based collision detection algorithm (50-100ms per query) --- faster than mesh reconstruction, especially on large point clouds In-contact configurations corresponds to a set with non-zero measure in C-space – more robust than mesh collision’s measure zero contact set.

47. Application to Motion Planning Use overall collision probability for the objects as a cost in motion planning algorithms to compute the trajectory with minimum collision probability

48. Other Applications The per-point collision probability is more useful – Provides finer control in terms of handling environment uncertainty – Can use work space information to guide the planning procedure in order to avoid collision

49. Conclusions A robust proximity computation and collision detection algorithm for noisy point cloud data Problem reduced to robust classification Initial results on point-cloud data from PR2 sensors

50. Future Work Currently we directly use point clouds, which can only model the space with/without obstacles Due to sensor range, part of the space is unknown So we will apply our algorithm to the data structure that can encode ‘unknown’ space, such as the octomap in ROS Also more useful for sensor with dense resolution, like kinect

51. Future Work Currently implementation is on static models Extend to dynamic environments – New objects added or deleted – Handle deformable objects and update sensor uncertainty SVM has incremental variations that can handle dynamic data

52. Acknowledge National Science Foundation Army Research Office Willow Garage

53. Thanks