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miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
Many problems in computer vision and robotics can be phrased as non-line...
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QRkit: Sparse, Composable QR Decompositions for Efficient and Stable Solutions to Problems in Computer Vision
Embedded computer vision applications increasingly require the speed and...
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Progressive Batching for Efficient Non-linear Least Squares
Non-linear least squares solvers are used across a broad range of offlin...
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Efficient Stochastic Programming in Julia
We present StochasticPrograms.jl, a user-friendly and powerful open-sour...
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Real-time Vision-based Depth Reconstruction with NVidia Jetson
Vision-based depth reconstruction is a challenging problem extensively s...
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Matrix Difference in Pose-Graph Optimization
Pose-Graph optimization is a crucial component of many modern SLAM syste...
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Ising Processing Units: Potential and Challenges for Discrete Optimization
The recent emergence of novel computational devices, such as adiabatic q...
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Least Squares Optimization: from Theory to Practice
Nowadays, Non-Linear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system, that addresses transparently problems with a different structure and designed to be easy to extend. The system is written in modern C++ and can run efficiently on embedded systems. Our package is available at https://gitlab.com/srrg-software/srrg2_solver. We validated our approach by conducting comparative experiments on several problems using standard datasets. The results show that our system achieves state-of-the-art performances in all tested scenarios.
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