Spinning Laser

Chitresh BhushanUndergraduate student, IIT Kharagpur, India

Under supervision of

Mike BosseSenior Research Scientist, QCAT,

CSIRO ICT Center, Brisbane

Objective

To detect and join the edges, in real-time,in 3D point cloud generated using range data from a single laser sensor, which can be used for mapping, navigation etc.

What is spinning laser ?

Simple laser sensor rotating in a plane perpendicular to scan-plane (as shown).

Provides :

Range information Angular Velocity Orientation of sensor

Pre-processing

Correcting data with calibration corrections.

Updating the timestamps to allow some clock-drift.

Validating all scans depending on updated timestamps of neighboring scans.

Filtering & Processing

Phantom discontinuity is removed with range data of neighboring point.

Data is median filtered for 5 points in each scan.

3D Cartesian coordinates, pose, normals & weights are calculated for each point using neighboring points.

Edge Detection I

Zero crossing of 2nd order derivative is used over the range data.

Laplacian of Gaussian (LoG) is used. (sensitive to edges with smoothing out the noise)

5x5 kernel is used with standard deviation of 0.7 .

0.083307 1.018699 2.123167 1.018699 0.0833071.018699 5.519411 0.300248 5.519411 1.0186992.123167 0.300248 -40.254 0.300248 2.1231671.018699 5.519411 0.300248 5.519411 1.0186990.083307 1.018699 2.123167 1.018699 0.083307

Edge Detection II LoG kernel is applied on last 5 scans & all  other processing

are done using last 2 scans, of which LoG values are known

False Edges I Theoretical error:

Presence of a zero crossing in between the actual edges.

f(x)=Gaussian of i(x)

C(x)=f '(x)

Ref: Clark, J. J., Authenticating edges produced by zero-crossing algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1):43-57 (1989).

False Edges II Very few points with zero LoG value

-127

0(Blue)

+127

False Edges III Practically too many zero crossing

-127

0(Blue)

+127

Removing False Edges I Using minimum threshold in difference of

LoG value at zero crossing. Removes false edges due to noise.

Points with +ve LoG value

Red: Required edge(nearer points)

Green: False edge

Range

Removing False Edges II

Nearer points are edges

Removing False Edges III Normal condition number: confidence of

normals calculated.

Ratio of two smallest eigen values of scatter matrix.

Lower normal condition number corresponds to a flat region.

Thresholding a minimum value for normal condition number removes false edges due to theoretical error in zero crossing.

Removing False Edges IV

Blue: Low normal cond. No Red: High normal cond. No

Fitting Planes

Joining edges Following the zero crossing & assigning

each edge point on that zero crossing same Edge ID.

Joining edge points with same ID, in correct order.

Edges crossing each others are NOT joined.

Results

Results

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Results

Live Demonstration !

Future Work I Direction of normals for edge detection

Edges, which do not have discontinuity in range values, can be detected by considering the direction of normals in neighboring points.

Future Work II Using more information from history

Edges are not joined when any one point in intermediate scan is missed. Looking beyond one scan line may solve the problem.

Future Work III Adaptive thresholds

Range values increases rapidly when scan points are at large distance from sensor (point P3 & P4, above) & it may lead to a false edge at P3. An adaptive (with range) threshold can fix the problem.

Future Work IV Hexagonal neighborhood

8-neighbors 6-neighbors

Acknowledgment

Mike Bosse Paul Flick Robert Zlot Felix Duvallet All QCAT staff

References J.L Lerma, J.M Biosca: Segmentation and filtering of laser scanner data for cultural

heritage. In CIPA, page 896, Torino, Italy, 2005 Qiang Ji, Robert M. Haralick: Quantitative Evaluation of Edge Detectors Using the

Minimum Kernel Variance Criterion. ICIP (2) 1999: 705-70 Yon-Lin Kok, Soheil I.Sayegh, Joo-Heng Hong: An Algorithm to Find Two-

Dimensional Signals with Specified Zero Crossings. IEEE Transactions on acoustics, speech and signal processing, Vol. ASSP-35, No. 1, January 1987: Page 107

Fangwei Zhaol, Christopher J.S. Desilva, Use of the Laplacian of Gaussian operator in prostate ultrasoundimage processing. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 20, No 2,1998

Boulaassal, H., Landes T., Grussenmeyer P., Tarsha-Kurdi F., Automatic Segmentation of Building Facades Using Terrestrial Laser Data, IAPRS Volume XXXVI, Part 3 / W52, 2007

Ben Weiss, Shell & Slate Software Corp. Fast Median and Bilateral Filtering, ACM Transactions on Graphics (TOG) 2006, 519 - 526

C. Chu, N. Nandhakumar, and J. K. Aggarwal, "Image segmentation using laser radar data,"Patt. Recogn., vol. 23, no. 6, pp. 569-581, 1990.

Questions ?

Thank You