EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association
By Michael Strecke, Jörg Stückler. 2019
89 citations recorded by the source corpus.
The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applications in robotics or augmented reality. In this paper, we propose a novel approach to dynamic SLAM with dense object-level representations. We represent rigid objects in local volumetric signed distance function (SDF) maps, and formulate multi-object tracking as direct alignment of RGB-D images with the SDF representations. Our main novelty is a probabilistic formulation which naturally leads to strategies for data association and occlusion handling. We analyze our approach in experiments and demonstrate that our approach compares favorably with the state-of-the-art methods in terms of robustness and accuracy.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Matthias Nießner
- Shuran Song
- Roozbeh Mottaghi
- Kiana Ehsani
- S. Gadre
- Niloy J. Mitra
- Changjian Li
- Yu-Shiang Wong
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Ming-Hsuan Yang
- Matthias Nießner
- Dacheng Tao
- Trevor Darrell
- Xiaogang Wang
- Abhinav Kumar Gupta
In Geology
- Matthias Nießner
- Shuran Song
- Niloy J. Mitra
- Changjian Li
- Kiana Ehsani
- Yu-Shiang Wong
- Angela Dai
- Leonidas J. Guibas
Citations in the corpus
- Continuous Scene Representations for Embodied AI (2022, background): “Dynamic SLAM approaches [41, 48, 60] have been proposed to handle moving objects in a scene.”
- RigidFusion: RGB‐D Scene Reconstruction with Rigidly‐moving Objects (2021, methodology): “For Airship object in Room4 sequence, both MaskFusion [RBA18] and EMFusion [SS19] fails to associating segments across frames and output a fragmented tra-jctories.”