SLAM++: Simultaneous Localisation and Mapping at the Level of Objects. Salas-Moreno, R., F., Newcombe, R., A., Strasdat, H., Kelly, P., H., J., & Davison, A., J. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013, 2013.
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Paper abstract bibtex
Paper abstract bibtex We present the major advantages of a new 'object ori-ented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, real-time 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph opti-misation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction , but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efficient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relo-calisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction.
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abstract = {We present the major advantages of a new 'object ori-ented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, real-time 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph opti-misation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction , but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efficient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relo-calisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction.},
bibtype = {article},
author = {Salas-Moreno, Renato F and Newcombe, Richard A and Strasdat, Hauke and Kelly, Paul H J and Davison, Andrew J},
journal = {2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013}
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