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Bridging the Gap Between Explicit and Implicit Representations: Cross-Data Association for VSLAM
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AI
DOI：10.1109/TITS.2024.3477570.png)
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Abstract
En 中文
Visual simultaneous localization and mapping (VSLAM) is a crucial technology in intelligent vehicles that relies on either explicit or implicit representations. Explicit methods are prevalent in real-time systems, offer precise geometric control, and are easy to visualize. However, they struggle with complex, dynamic environments and require high storage capacity. On the other hand, implicit techniques excel in handling intricate, changing shapes due to their compact representation and inference ability while requiring more complex display and rendering processes. A combination of both types of representations could significantly enhance the performance of VSLAM, but the cross-data association method for standalone explicit and implicit representations is still lacking. To this end, this paper proposes a data association scheme that bridges the gap between explicit and implicit representations by individually modeling the uncertainties in each representation. Our approach features a multi-level feature selection process tailored for data association. It initially extracts coarse-level features during explicit representation generation based on Bayesian estimation and refines them using the implicit representation based on ray sampling, which enhances robustness while reducing rendering costs. We rigorously evaluated our proposed methodology against current state-of-the-art approaches using public datasets and real robot scenes. The results show that our coarse-to-fine feature selection method outperforms existing techniques both quantitatively and qualitatively, suggesting its potential to significantly boost the contemporary VSLAM system performance.
Keywords:
Feature extraction
Rendering (computer graphics)
Uncertainty
Shape
Geometry
Bayes methods
Accuracy
Three-dimensional displays
Point cloud compression
Optimization
VSLAM
explicit representation
implicit representation
data association
uncertainty modeling
feature selection
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