Object Detection Based on Plane Segmentation and Features Matching for a Service Robot

With the aging of the world population and the
continuous growth in technology, service robots are more and more
explored nowadays as alternatives to healthcare givers or personal
assistants for the elderly or disabled people. Any service robot
should be capable of interacting with the human companion, receive
commands, navigate through the environment, either known or
unknown, and recognize objects. This paper proposes an approach
for object recognition based on the use of depth information and
color images for a service robot. We present a study on two of the
most used methods for object detection, where 3D data is used to
detect the position of objects to classify that are found on horizontal
surfaces. Since most of the objects of interest accessible for service
robots are on these surfaces, the proposed 3D segmentation reduces
the processing time and simplifies the scene for object recognition.
The first approach for object recognition is based on color histograms,
while the second is based on the use of the SIFT and SURF feature
descriptors. We present comparative experimental results obtained
with a real service robot.

Authors:

• António J. R. Neves
• Rui Garcia
• Paulo Dias
• Alina Trifan

Keywords:

• Service Robot
• Object Recognition
• 3D Sensors
• Plane Segmentation.

References:

[1] L. Martinez, P. Loncomilla, and P. Ruiz-del Solar, “Object recognition
for manipulation tasks in real domestic settings: A comparative study,”
in Proceedings of RoboCup 2014 Symposium, Joao Pessoa, Brazil, July
2014.
[2] “RoboCup Federation official website,” www.robocup.org, accessed:
2015-09-30.
[3] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,”
Int. J. Comput. Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004.
[4] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust
features (surf),” Comput. Vis. Image Underst., vol. 110, no. 3, pp.
346–359, Jun. 2008.
[5] R. B. Rusu, Z. C. Marton, N. Blodow, and M. Beetz, “Persistent Point
Feature Histograms for 3D Point Clouds,” in Proceedings of the 10th
International Conference on Intelligent Autonomous Systems (IAS-10),
Baden-Baden, Germany, 2008.
[6] J. Borenstein and Y. Koren, “The Vector Field Histogram - Fast Obstacle
Avoidance for Mobile Robots,” IEEE Transactions on Robotics and
Automation, vol. 7, no. 3, pp. 278–288, 1991.
[7] R. B. Rusu, A. Holzbach, G. Bradski, and M. Beetz, “Detecting and
segmenting objects for mobile manipulation,” in Proceedings of IEEE
Workshop on Search in 3D and Video (S3DV), held in conjunction with
the 12th IEEE International Conference on Computer Vision (ICCV),
Kyoto, Japan, September 27 2009.
[8] R. B. Rusu and S. Cousins, “3d is here: Point cloud library (pcl),” in
International Conference on Robotics and Automation, Shanghai, China,
2011 2011.
[9] Y. Holdstein and A. Fischer, “Three-dimensional surface reconstruction
using meshing growing neural gas (mgng),” The Visual Computer,
vol. 24, no. 4, pp. 295–302, 2008.
[10] R. Dwiputra, M. F¨uller, F. Hegger, S. Schneider, I. A. J. M. S. Loza,
A. Y. Ozhigov, S. Biswas, N. V. Deshpand, A. H. I. Ivanovska, P. G.
Ploeger, and G. K. Kraetzschmar, “The b-it-bots robocup@home 2014
team description paper,” Joao Pessoa, Brazil, 2014.
[11] S. A. M. C. J. J. M. Lunenburg and T. T. J. Derksen, “Tech united
eindhoven @home 2014 team description paper,” Joao Pessoa, Brazil,
2014.
[12] T. D. Jager, “Robust object detection for service robotics,” PhD thesis,
Utrecht University, 2013.
[13] “Robot Operating System official website,” www.ros.org, accessed:
2015-09-30.
[14] J. St¨uckler and S. Behnke, “Multi-resolution surfel maps for efficient
dense 3d modeling and tracking,” J. Vis. Comun. Image Represent.,
vol. 25, no. 1, pp. 137–147, Jan. 2014.
[15] J. St¨uckler, B. Waldvogel, H. Schulz, and S. Behnke, “Dense real-time
mapping of object-class semantics from rgb-d video,” Journal of
Real-Time Image Processing, 2014.
[16] “CAMBADA@HOME official website,” http://robotica.ua.pt/
CAMBADA@HOME/, accessed: 2015-09-30.
[17] “Microsoft Kinect official website,” https://dev.windows.com/en-us/
kinect, accessed: 2015-09-30.
[18] “Point Cloud Library official website,” http://pointclouds.org/, accessed:
2015-09-30.
[19] M. A. Fischler and R. C. Bolles, “Random sample consensus: A
paradigm for model fitting with applications to image analysis and
automated cartography,” Commun. ACM, vol. 24, no. 6, pp. 381–395,
1981.
[20] D. G. R. Bradski and A. Kaehler, Learning Opencv, 1st Edition, 1st ed.
O’Reilly Media, Inc., 2008.
[21] M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vision,
vol. 7, no. 1, pp. 11–32, Nov. 1991.
[22] B. Schiele and J. L. Crowley, “Object recognition using
multidimensional receptive field histograms,” in Proceedings of
the 4th European Conference on Computer Vision-Volume I - Volume
I, ser. ECCV ’96. London, UK: Springer-Verlag, 1996, pp. 610–619.
[23] M. Muja and D. G. Lowe, “Fast approximate nearest neighbors
with automatic algorithm configuration,” in In VISAPP International
Conference on Computer Vision Theory and Applications, 2009, pp.
331–340.