From 2D to 3D: Monocular Vision With application to robotics/ar

Transcription

1 From 2D to 3D: Monocular Vision With application to robotics/ar

2 Motivation How many sensors do we really need?

3 Motivation What is the limit of what can be inferred from a single embodied (moving) camera frame?

4

5 Aim AR with a hand-held camera Visual Tracking provides registration Track without prior model of world Challenges Speed Accuracy Robustness Interaction with real world

6 Existing attempts: SLAM Simultaneous Localization and Mapping Well-established in robotics (using a rich array of sensors) Demonstrated with a single hand-held camera by Davison 2003

7 Model-based tracking vs SLAM

8 Model-based tracking vs SLAM Model-based tracking is More robust More accurate Why? SLAM fundamentally harder?

9 Pinhole camera model X, Y, Z fx / Z, fy / Z [ X fx f Y fy = Z Z 1 f ] X 0 Y 0 Z x = PX

10 Pinhole camera model principal point:( p x, f X + Zp x f f Y + Zp x = Z f K = f f py ) X p x 1 0 Y py 1 0 Z px p y calibration matrix 1 P = K [ I 0]

11 Camera rotation and translation In non-homogeneous coordinates: ( ~ ~ ~ X cam = R X - C X cam x = K[ I 0] X cam ) ~ ~ ~ R RC X R RC = = X [ ] ~ = K R RC X P = K[ R t ], ~ t = RC Note: C is the null space of the camera projection matrix (PC=0)

12 Triangulation Given projections of a 3D point in two or more images (with known camera matrices), find the coordinates of the point X? x1 O1 x2 O2

13 Structure from Motion (SfM) Given: m images of n fixed 3D points xij = Pi Xj, i = 1,, m, j = 1,, n Problem: estimate m projection matrices Pi and n 3D points Xj from the mn correspondences xij Xj x1j P1 x3j x2j P3 P2

14 SfM ambiguity If we scale the entire scene by some factor k and, at the same time, scale the camera matrices by the factor of 1/k, the projections of the scene points in the image remain exactly the same: 1 x = PX = P (k X) k It is impossible to recover the absolute scale of the scene!

15 Structure from Motion (SfM) Given: m images of n fixed 3D points xij = Pi Xj, i = 1,, m, j = 1,, n Problem: estimate m projection matrices Pi and n 3D points Xj from the mn correspondences xij With no calibration info, cameras and points can only be recovered up to a 4x4 projective transformation Q: X QX, P PQ-1 We can solve for structure and motion when 2mn >= 11m +3n For two cameras, at least 7 points are needed

16 Bundle Adjustment Non-linear method for refining structure and motion (Levenberg-Marquardt) Minimizing re-projection error Xj E (P, X) = D( x ij, Pi X j ) m n i =1 j =1 P1Xj P1 x3j x1j P2 Xj x2j P3 Xj P3 P2 2

17 Self-calibration Self-calibration (auto-calibration) is the process of determining intrinsic camera parameters directly from uncalibrated images For example, when the images are acquired by a single moving camera, we can use the constraint that the intrinsic parameter matrix remains fixed for all the images Compute initial projective reconstruction and find 3D projective transformation matrix Q such that all camera matrices are in the form Pi = K [Ri ti] Can use constraints on the form of the calibration matrix: zero skew

18 Why is this cool?

19 Why is this still cool?

20 The SLAM Problem Simultaneous Localization And Mapping A robot is exploring an unknown, static environment Given: The robot's controls Observations of nearby features Estimate: Map of features Path of the robot

21 Structure of the landmark-based SLAM Problem

22 SLAM a hard problem?? SLAM: robot path and map are both unknown Robot path error correlates errors in the map

23 SLAM a hard problem?? Robot pose uncertainty In the real world, the mapping between observations and landmarks is unknown Picking wrong data associations can have catastrophic consequences Pose error correlates data associations

24 SLAM Full SLAM: Estimates entire path and map! p ( x1:t, m z1:t, u1:t ) Online SLAM: p ( xt, m z1:t, u1:t ) = p ( x1:t, m z1:t, u1:t ) dx1dx2...dxt 1 Integrations typically done one at a time Estimates most recent pose and map!

25 Graphical Model of Full SLAM p ( x1:t, m z1:t, u1:t )

26 Graphical Model of Online SLAM p ( xt, m z1:t, u1:t ) = p ( x1:t, m z1:t, u1:t ) dx1 dx2...dxt 1

27 Scan Matching Maximize the likelihood of the i-th pose and map relative to the (i-1)-th pose and map { xˆt = arg max p( zt xt, mˆ xt current measurement [ t 1] } ) p( xt ut 1, xˆt 1 ) robot motion map constructed so far Calculate the map according to mapping with known poses based on the poses and observations

28 SLAM approach

29 PTAM approach

30 Tracking & Mapping threads

31 Mapping thread

32 Stereo Initialization 5 point-pose algorithm (Stewenius et al '06) Requires a pair of frames and feature correspondences Provides initial (sparse) 3D point cloud

33 Wait for new keyframe Keyframes are only added if: There is a baseline to the other keyframes Tracking quality is good When a keyframe is added: The mapping thread stops whatever it is doing All points in the map are measured in the keyframe New map points are found and added to the map

34 Add new map points Want as many map points as possible Check all maximal FAST corners in the keyframe: Check Shi-Tomasi score Check if already in map Epipolar search in a neighboring keyframe Triangulate matches and add to map Repeat in four image pyramid levels

35 Optimize map Use batch SFM method: Bundle Adjustment* Adjusts map point positions and keyframe poses Minimizes re-projection error of all points in all keyframes (or use only last N keyframes) Cubic complexity with keyframes, linear with map points Compatible with M-estimators (we use Tukey)

36 Map maintenance When camera is not exploring, mapping thread has idle time use this to improve the map Data association in bundle adjustment is reversible Re-attempt outlier measurements Try to measure new map features in all old keyframes

37 Tracking thread

38 Pre-process frame Make mono and RGB version of image Make 4 pyramid levels Detect FAST corners

39 Project Points Use motion model to update camera pose Project all map points into image to see which are visible, and at what pyramid level Choose subset to measure ~50 biggest features for coarse stage 1000 randomly selected for fine stage

40 Measure Points Generate 8x8 matching template (warped from source keyframe) Search a fixed radius around projected position Use zero-mean SSD Only search at FAST corner points Up to 10 inverse composition iterations for subpixel position (for some patches) Typically find 60-70% of patches

41 Update camera pose 6-DOF problem 10 iterations Tukey M-Estimator to minimize a robust objective function of re-projection error where ej is the re-projection error vector

42 Bundle-adjustment Global bundle-adjustment Local bundle-adjustment X - The newest 5 keyframes in the keyframe chain Z - All of the map points visible in any of these keyframes Y - Keyframe for which a measurement of any point in Z has been made That is, local bundle Optimizes the pose of the most recent keyframe and its closest neighbors, and all of the map points seen by these, using all of the measurements ever made of these points.

43 Video

44 Capabilities

45 Capabilities Bundle adjusted point cloud with PTAM Multi-scale Compactly Supported Basis Functions

46 Video

47 RGB-D Sensor Principle: structured light IR projector + IR camera RGB camera Dense depth images

48 Kinect-based mapping

49 System Overview Frame-to-frame alignment Global optimization (SBA for loop closure)

50 Feature matching

51 RANSAC Features correspondences are established; outliers robustly removed Homography (Transformation) between the two keyframes can now be estimated

52 Global Optimization (RGBD-ICP)

53 Benefits Visual and depth information used jointly for real-time mapping application Reconstruct a dense map of the environment Avoid dense stereo for every pair of KeyFrames Optimize over sparse set of feature points Results in dramatic speed improvements Allows for computing other valuable algorithms simultaneously (e.g. navigation, obstacle avoidance, scene understanding)

54 Video

55 Kinect + Real-time reconstruction

56 Video

57 Conclusion So much information available from a single camera Yet to truly understand what we can infer from a single camera Several exciting technologies in the recent past Software problem; not a hardware limitation Monocular vision can be sufficient for a lot of use cases

58 Thanks!