Hierarchical map building using visual landmarks and geometric constraints Export

@inproceedings{zivkovic-etal-iros05, abstract = {This paper addresses the problem of automatic construction of a hierarchical map from images. Our approach departs from a large collection of omnidirectional images taken at many locations in a building. First, a low-level map is built that consists of a graph in which relations between images are represented. For this, we use a metric based on visual landmarks (SIFT features) and geometrical constraints. Then, we use a graph partitioning method to cluster nodes and in this way construct the high-level map. Experiments on real data show that meaningful higher and lower level maps are obtained, which can be used for accurate localization and planning.}, author = {Zivkovic, Z. and Bakker, B. and Krose, B.}, booktitle = {Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on}, citeulike-article-id = {4325498}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/IROS.2005.1544951}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1544951}, doi = {10.1109/IROS.2005.1544951}, journal = {Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on}, keywords = {graph, normcut, slam}, pages = {2480--2485}, posted-at = {2009-04-16 12:07:03}, priority = {2}, title = {Hierarchical map building using visual landmarks and geometric constraints}, url = {http://dx.doi.org/10.1109/IROS.2005.1544951}, year = {2005} }

TY - CONF ID - zivkovic-etal-iros05 L3 - citeulike-article-id:4325498 N2 - This paper addresses the problem of automatic construction of a hierarchical map from images. Our approach departs from a large collection of omnidirectional images taken at many locations in a building. First, a low-level map is built that consists of a graph in which relations between images are represented. For this, we use a metric based on visual landmarks (SIFT features) and geometrical constraints. Then, we use a graph partitioning method to cluster nodes and in this way construct the high-level map. Experiments on real data show that meaningful higher and lower level maps are obtained, which can be used for accurate localization and planning. JF - Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on EP - 2485 TI - Hierarchical map building using visual landmarks and geometric constraints T2 - Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on SP - 2480 KW - graph KW - normcut KW - slam AU - Zivkovic, Z AU - Bakker, B AU - Krose, B PY - 2005/// UR - http://dx.doi.org/10.1109/IROS.2005.1544951 ER -