Bundle Adjustment in the Eager Mode
Zitong Zhan, Huan Xu, Zihang Fang, Xinpeng Wei, Yaoyu Hu, Chen Wang

TL;DR
This paper introduces a GPU-accelerated, eager-mode bundle adjustment library integrated with PyTorch, significantly improving runtime efficiency for robotic applications like SLAM and AR.
Contribution
It presents a novel eager-mode BA library with native PyTorch integration, sparsity-aware auto-differentiation, and GPU acceleration for enhanced performance.
Findings
Achieves an average speedup of 18.5x over GTSAM
Achieves an average speedup of 22x over g2o
Achieves an average speedup of 23x over Ceres
Abstract
Bundle adjustment (BA) is a critical technique in various robotic applications such as simultaneous localization and mapping (SLAM), augmented reality (AR), and photogrammetry. BA optimizes parameters such as camera poses and 3D landmarks to align them with observations. With the growing importance of deep learning in perception systems, there is an increasing need to integrate BA with deep learning frameworks for enhanced reliability and performance. However, widely-used C++-based BA libraries, such as GTSAM, go, and Ceres Solver, lack native integration with modern deep learning libraries like PyTorch. This limitation affects their flexibility, ease of debugging, and overall implementation efficiency. To address this gap, we introduce an eager-mode BA library seamlessly integrated with PyTorch with high efficiency. Our approach includes a sparsity-aware auto-differentiation design…
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Taxonomy
TopicsVibration and Dynamic Analysis · Structural Engineering and Vibration Analysis
MethodsALIGN
