GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Die Hauptfunktionen von borglab/gtsam sind: Robotics Algorithms, SLAM and Mapping, Optimization Libraries, Robotics Libraries.
Open-Source-Alternativen zu borglab/gtsam sind unter anderem: ceres-solver/ceres-solver — Ceres Solver is a C++ library for numerical optimization, specializing in non-linear least squares and unconstrained… robotlocomotion/drake — Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and… davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… fields2cover/fields2cover — Robust and efficient coverage paths for autonomous agricultural vehicles. A modular and extensible Coverage Path… gisbi-kim/nano-pgo — Figure 1: Example Results Visualization. More examples can be found here. facebookresearch/theseus — A library for differentiable nonlinear optimization.
Ceres Solver is a C++ library for numerical optimization, specializing in non-linear least squares and unconstrained optimization problems. It serves as a framework for automatic differentiation and robust curve fitting, providing tools to solve large-scale mathematical models. The library is distinguished by its bundle adjustment capabilities, which exploit sparse matrix structures to refine 3D scene points and camera parameters. It utilizes dual-number automatic differentiation to compute derivatives of cost functions, removing the need for manual Jacobian derivation. The project covers a
Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ
dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach
A library for differentiable nonlinear optimization