Towards semantic maps for mobile robots
Robotics and Autonomous Systems
Shape retrieval with eigen-CSS search
Image and Vision Computing
Unsupervised learning of 3D object models from partial views
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robust on-line model-based object detection from range images
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Model-based object recognition from 3D laser data
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Tomographic reconstruction from fewer projections
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
View-Invariant object detection by matching 3d contours
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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This paper presents a novel object recognition approach based on range images. Due to its insensitivity to illumination, range data is well suited for reliable silhouette extraction. Silhouette or contour descriptions are good sources of information for object recognition. We propose a complete object recognition system, based on a 3D laser scanner, reliable contour extraction with floor interpretation, feature extraction using a new, fast Eigen-CSS method, and a supervised learning algorithm. The recognition system was successfully tested on range images acquired with a mobile robot, and the results are compared to standard techniques, i.e., Geometric features, Hu and Zernike moments, the Border Signature method and the Angular Radial Transformation. An evaluation using the receiver operating characteristic analysis completes this paper. The Eigen-CSS method has proved to be comparable in detection performance to the top competitors, yet faster than the best one by an order of magnitude in feature extraction time.