miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
This provides a more agile development tool for researchers and engineers in computer vision and robotics working on problems like SLAM and SfM, but it is incremental as it builds on existing optimization methods with added flexibility.
The authors tackled the need for a flexible framework for non-linear least squares optimization in computer vision and robotics by developing miniSAM, an open-source C++/Python tool with a full Python/NumPy API and support for multiple sparse linear solvers, including CUDA-enabled ones, which offers comparable performance to existing frameworks.
Many problems in computer vision and robotics can be phrased as non-linear least squares optimization problems represented by factor graphs, for example, simultaneous localization and mapping (SLAM), structure from motion (SfM), motion planning, and control. We have developed an open-source C++/Python framework miniSAM, for solving such factor graph based least squares problems. Compared to most existing frameworks for least squares solvers, miniSAM has (1) full Python/NumPy API, which enables more agile development and easy binding with existing Python projects, and (2) a wide list of sparse linear solvers, including CUDA enabled sparse linear solvers. Our benchmarking results shows miniSAM offers comparable performances on various types of problems, with more flexible and smoother development experience.