Incremental scene understanding on dense SLAM

Li C, Xiao H, Tateno K, Tombari F, Navab N, Hager GD (2016)

Publication Type: Conference contribution

Publication year: 2016

Journal

IEEE/RSJ International Conference on Intelligent Robots and Systems IEEE

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2016-November

Pages Range: 574-581

Conference Proceedings Title: IEEE International Conference on Intelligent Robots and Systems

Event location: Daejeon, KOR

ISBN: 9781509037629

DOI: 10.1109/IROS.2016.7759111

Abstract

We present an architecture for online, incremental scene modeling which combines a SLAM-based scene understanding framework with semantic segmentation and object pose estimation. The core of this approach comprises a probabilistic inference scheme that predicts semantic labels for object hypotheses at each new frame. From these hypotheses, recognized scene structures are incrementally constructed and tracked. Semantic labels are inferred using a multi-domain convolutional architecture which operates on the image time series and which enables efficient propagation of features as well as robust model registration. To evaluate this architecture, we introduce a large-scale RGB-D dataset JHUSEQ-25 as a new benchmark for the sequence-based scene understanding in complex and densely cluttered scenes. This dataset contains 25 RGB-D video sequences with 100,000 labeled frames in total. We validate our method on this dataset and demonstrate improved performance of semantic segmentation and 6-DoF object pose estimation compared with methods based on the single view.

Involved external institutions

Johns Hopkins University (JHU) US United States (USA) (US) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Li, C., Xiao, H., Tateno, K., Tombari, F., Navab, N., & Hager, G.D. (2016). Incremental scene understanding on dense SLAM. In IEEE International Conference on Intelligent Robots and Systems (pp. 574-581). Daejeon, KOR: Institute of Electrical and Electronics Engineers Inc..

MLA:

Li, Chi, et al. "Incremental scene understanding on dense SLAM." Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, KOR Institute of Electrical and Electronics Engineers Inc., 2016. 574-581.

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