The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Fusion++: Volumetric Object-Level SLAM
Abstract
We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to initialise compact per-object Truncated Signed Distance Function (TSDF) reconstructions with object size-dependent resolutions and a novel 3D foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation. Objects are incrementally refined via depth fusion, and are used for tracking, relocalisation and loop closure detection. Loop closures cause adjustments in the relative pose estimates of object instances, but no intra-object warping. Each object also carries semantic information which is refined over time and an existence probability to account for spurious instance predictions. We demonstrate our approach on a hand-held RGB-D sequence from a cluttered office scene with a large number and variety of object instances, highlighting how the system closes loops and makes good use of existing objects on repeated loops. We quantitatively evaluate the trajectory error of our system against a baseline approach on the RGB-D SLAM benchmark, and qualitatively compare reconstruction quality of discovered objects on the YCB video dataset. Performance evaluation shows our approach is highly memory efficient and runs online at 4-8Hz (excluding relocalisation) despite not being optimised at the software level.
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Cites
Deep Residual Learning for Image Recognition
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
ORB: An efficient alternative to SIFT or SURF
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
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KinectFusion: Real-time dense surface mapping and tracking
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Self-citationA benchmark for the evaluation of RGB-D SLAM systems
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Self-citationA volumetric method for building complex models from range images
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ShapeNet: An Information-Rich 3D Model Repository
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Huang, C., Mees, O., Zeng, A., & Burgard, W. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160969 · Cited by 376
RLBench: The Robot Learning Benchmark & Learning Environment
James, S., Ma, Z., Arrojo, D. R., & Davison, A. J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2974707 · Cited by 405
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Gu, Q., Kuwajerwala, A., Morin, S., Jatavallabhula, K. M., Sen, B., Agarwal, A., Rivera, C., Paul, W., Ellis, K., Chellappa, R., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610243 · Cited by 406
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Metadata
- Authors
- Mccormac, J., Clark, R., Bloesch, M., Davison, A., & Leutenegger, S.
- Venue
- 2018 International Conference on 3D Vision (3DV)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 32-41
- OpenAlex
- W2888144883
- Cited by
- 308
- References
- 38
- FWCI
- 57.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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