To overcome the problem of a single image source, complex processing and inaccurate positioning, a visual identification and location algorithm based on multi-modal information is proposed, and the fusion processing is performed by extracting the multimodal information of the two-dimensional image and the point cloud image to realize object recognition and positioning. Firstly the target 2D image information is obtained by RGB camera. The contour is recognized through the contour detection and matching process. Then the image SIFT feature is extracted for location tracking and the position of the object is obtained. Meanwhile obtaining a point cloud image by RGB-D camera and the best model can be sorted through pre-processing, Euclidean cluster segmentation, computing VFH feature and KD-tree searching, identifying the point cloud image. Then the orientation is obtained by registering the point clouds. Finally, the two-dimensional images and point cloud image are used to process object information, complete the identification and positioning of the target. The effect of the method is verified by the robotic gripping experiment. The result shows that the multi-modal information of two-dimensional image and point cloud image can be used to identify and locate different target objects. Compared with the processing method using only two-dimensional or point cloud single-mode image information, the positioning error can be reduced to 50%, the robustness and accuracy are better.
Visual identification and location algorithm for robot based on the multimodal information
First published at:Feb 22, 2018
1 Collet A, Srinivasa S S. Efficient multi-view object recognition and full pose estimation[C]//Proceeding of 2010 IEEE International Conference on Robotics and Automation, 2010: 2050-2055.
2 Munoz E, Konishi Y, Murino V, et al. Fast 6D pose estimation for texture-less objects from a single RGB image[C]//Proceeding of 2016 IEEE International Conference on Robotics and Automation, 2016: 5623-5630.
3 Zhu M L, Derpanis K G, Yang Y F, et al. Single Image 3D object detection and pose estimation for grasping[C]//Proceeding of 2014 IEEE International Conference on Robotics and Automation, 2014: 3936-3943.
4 Bo L F, Lai K, Ren X F, et al. Object recognition with hierarchical kernel descriptors[C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition, 2011: 1729-1736.
5 Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration[C]//Proceeding of 2009 IEEE International Conference on Robotics and Automation, 2009: 3212-3217.
6 Braun M, Rao Q, Wang Y K, et al. Pose-RCNN: Joint object detection and pose estimation using 3D object proposals[C]// Proceeding of the 19th International Conference on Intelligent Transportation Systems (ITSC), 2016: 1546-1551.
7 Pavlakos G, Zhou X W, Chan A, et al. 6-DoF object pose from semantic keypoints[C]//Proceeding of 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017: 2011-2018.
8 Duan J, Gao X. Adaptive statistical filtering double threshholds based on improved canny operator edge detection algorithm[J]. Laser Journal, 2015, 36(1): 10-12.
9 Sánchez-Torrubia M G, Torres-Blanc C, López-Martínez M A. Pathfinder: A visualization eMathTeacher for actively learning Dijkstra's algorithm[J]. Electronic Notes in Theoretical Computer Science, 2009, 224: 151-158. DOI:10.1016/j.entcs.2008.12.059
10 Richtsfeld A, Vincze M. Basic object shape detection and tracking using perceptual organization[C]//Proceeding of 2009 IEEE International Conference on Advanced Robotics, 2009: 1-6.
11 Mörwald T, Prankl J, Richtsfeld A, et al. BLORT -the blocks world robotic vision toolbox[Z]. 2010: 1-8.
12 DAI J L. A research on preprocessing algorithms of mass point cloud[D]. Hangzhou: Zhejiang University, 2006.
13 Rusu R B, Cousins S. 3D is here: Point Cloud Library (PCL)[C]//Proceeding of 2011 IEEE International Conference on Robotics and Automation, 2011: 1-4.
14 Zhao T, Li H, Cai Q, et al. Point Cloud Segmentation Based on FPFH Features[C]// Proceedings of 2016 Chinese Intelligent Systems Conference. Singapore, Springer, 2016.
15 Rusu R B, Bradski G, Thibaux R, et al. Fast 3D recognition and pose using the viewpoint feature histogram[C]//Proceeding of 2010 IEEE International Conference on Intelligent Robots and Systems, 2010: 2155-2162.
16 Zhu D H. Point cloud library PCL[M]. Beijing: Beijing University of Aeronautics and Astronautics Press, 2012: 338-355.
Supported by National Natural Science Foundation of China (51305214), the Public Technology Application Project of Zhejiang (2017C31094) and the Natural Science Foundation of Ningbo (2017A610124)
Get Citation: Wei Yufeng, Liang Dongtai, Liang Dan, et al. Visual identification and location algorithm for robot based on the multimodal information[J]. Opto-Electronic Engineering, 2018, 45(2): 170650-1-170650-12.