miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework

Bibliographic Details
Title:	miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
Authors:	Dong, Jing, Lv, Zhaoyang
Publication Year:	2019
Collection:	Computer Science
Subject Terms:	Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
More Details:	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. Comment: Accepted in IROS 2019 PPNIV workshop
Document Type:	Working Paper
Access URL:	http://arxiv.org/abs/1909.00903
Accession Number:	edsarx.1909.00903
Database:	arXiv

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