IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, MO, USA.
Pedro Núñez, R. Vázquez-Martín, A. Bandera, F. Sandoval. «Combined Constraint Matching Algorithm for Stereo Visual Odometry based on Local Interest Points». IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, MO, USA. 2009.
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B. S. A. . F. {Pedro Núñez R. Vázquez-Martín, «Combined Constraint Matching Algorithm for Stereo Visual Odometry based on Local Interest Points,» in 2009 ieee/rsj international conference on intelligent robots and systems, iros 2009, 2009.
[Bibtex]@inproceedings{nunnez-laser, abstract = {In this paper, we describe a new approach which uses scale-invariant image features to estimate the motion of a stereo head. These point features are matched between pairs of frames and linked into image trajectories at video rate, generating what it is called visual odometry, i.e. motion estimates from visual input alone. With respect to previously proposed approaches, the main novelty of our proposal is that the matching between sets of features associated to stereo pairs and between sets of image features associated to consecutive frames are conducted by means of a fast combined constraint matching algorithm. Besides, the efficiency of the approach is increased by using a closed-form solution to estimate the final robot displacement between consecutive acquired frames. We have tested the proposed approach for navigational purposes in a real environment. Experimental results demonstrate the performance of the proposal. {\textcopyright} 2009 IEEE.}, author = {{Pedro N{\'{u}}{\~{n}}ez, R. V{\'{a}}zquez-Mart{\'{i}}n, A. Bandera}, F. Sandoval}, booktitle = {2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009}, doi = {10.1109/IROS.2009.5354667}, isbn = {9781424438044}, title = {{Combined Constraint Matching Algorithm for Stereo Visual Odometry based on Local Interest Points}}, year = {2009} }
Nuñez Trujillo, P. M., Drews Jr, P., Rocha, R., Campos, M. & Dias, J. «Novelty Detection and 3D Shape Retrieval based on Gaussian Mixture Models for Autonomous Surveillance Robotics». Proceedings, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, MO, USA.
- P. Nunez, P. Drews, R. Rocha, M. Campos, and J. Dias, «Novelty Detection and 3D Shape Retrieval Based on Gaussian Mixture Models for Autonomous Surveillance Robotics,» in Ieee/rsj international conference on intelligent robots and systems, (iros’09), 2009, p. pp. 4724–4730.
[Bibtex]@inproceedings{nunnez-retrieva-gauss, abstract = {This paper describes an efficient method for retrieving the 3-dimensional shape associated to novelties in the environment of an autonomous robot, which is equipped with a laser range finder. First, changes are detected over the point clouds using a combination of the Gaussian mixture model (GMM) and the earth mover's distance (EMD) algorithms. Next, the shape retrieval is achieved using two different algorithms. First, new samplings are generated from each Gaussian function, followed by a random sampling consensus (RANSAC) algorithm to retrieve geometric primitives. Furthermore, a new algorithm is developed to directly retrieve the shape according to the mathematical space of Gaussian mixture. In this paper, the set of geometric primitives has been limited to the set C = {sphere, cylinder, plane}. The two shape retrieval methods are compared in terms of computational cost and accuracy. Experimental results in various real and simulated scenarios demonstrate the feasibility of the approach.}, author = {Nunez, P and Drews, P and Rocha, R and Campos, M and Dias, J}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS'09)}, doi = {10.1109/iros.2009.5354712}, isbn = {9781424438044}, keywords = {3D shape retrieval,Gaussian mixture models,Gaussian processes,autonomous surveillance robotics,earth mover distance algorithms,geometric primitive retrieval,image retrieval,mobile robots,object detection,random sampling consensus}, pages = {pp. 4724--4730}, title = {{Novelty Detection and 3D Shape Retrieval Based on Gaussian Mixture Models for Autonomous Surveillance Robotics}}, year = {2009} }
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