gtsam

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.

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Quick Overview

GTSAM is an open-source C++ library that implements smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm. It provides a set of nonlinear factor types and inference algorithms to solve simultaneous localization and mapping (SLAM) and structure from motion (SFM) problems.

Pros

High performance and efficiency due to its C++ implementation
Flexible and extensible architecture allowing for custom factors and noise models
Comprehensive documentation and examples for various use cases
Active community and ongoing development

Cons

Steep learning curve for beginners due to its complex mathematical foundations
Limited support for real-time applications compared to some alternatives
Primarily focused on C++, with limited bindings for other languages
Can be computationally intensive for large-scale problems

Code Examples

Creating a simple 2D pose graph:

#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/Values.h>

using namespace gtsam;

int main() {
    NonlinearFactorGraph graph;
    Values initialEstimate;

    // Add a prior on the first pose, setting it to the origin
    Pose2 priorMean(0.0, 0.0, 0.0);
    noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
    graph.add(PriorFactor<Pose2>(1, priorMean, priorNoise));

    // Add odometry factors
    Pose2 odometry(2.0, 0.0, 0.0);
    noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
    graph.add(BetweenFactor<Pose2>(1, 2, odometry, odometryNoise));

    // Initialize the first pose at the origin
    initialEstimate.insert(1, Pose2(0.0, 0.0, 0.0));
    initialEstimate.insert(2, Pose2(2.0, 0.0, 0.0));

    // Optimize using Levenberg-Marquardt optimization
    LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
    Values result = optimizer.optimize();

    return 0;
}

Adding a landmark to a 2D SLAM problem:

#include <gtsam/slam/BearingRangeFactor.h>

// ... (previous includes and setup)

int main() {
    // ... (previous graph and initial estimate setup)

    // Add a landmark
    Point2 landmark(5.0, 1.0);
    initialEstimate.insert(L1, landmark);

    // Add a measurement to the landmark
    Rot2 bearing = Rot2::fromDegrees(45);
    double range = 5.0;
    graph.add(BearingRangeFactor<Pose2, Point2>(1, L1, bearing, range, measurementNoise));

    // ... (optimization and result handling)
}

Using ISAM2 for incremental SLAM:

#include <gtsam/nonlinear/ISAM2.h>

// ... (previous includes and setup)

int main() {
    ISAM2Params parameters;
    parameters.relinearizeThreshold = 0.01;
    parameters.relinearizeSkip = 1;
    ISAM2 isam(parameters);

    for (int i = 0; i < numPoses; ++i) {
        NonlinearFactorGraph newFactors

Competitor Comparisons

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Pros of Ceres Solver

More flexible problem formulation, allowing for a wider range of optimization problems
Better performance for large-scale problems with sparse structure
More extensive documentation and examples

Cons of Ceres Solver

Steeper learning curve, especially for users new to optimization
Less intuitive API for factor graph problems compared to GTSAM
Requires more manual setup for certain types of problems

Code Comparison

GTSAM example (factor graph):

NonlinearFactorGraph graph;
graph.add(PriorFactor<Pose3>(X(1), pose_prior, prior_noise));
graph.add(BetweenFactor<Pose3>(X(1), X(2), odom, odom_noise));

Ceres Solver example (general optimization):

ceres::Problem problem;
problem.AddResidualBlock(
    new ceres::AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor),
    nullptr,
    &x);

Both libraries are powerful optimization tools, but GTSAM is more specialized for factor graphs and SLAM problems, while Ceres Solver offers more flexibility for general optimization tasks.

g2o

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Pros of g2o

More flexible and extensible architecture, allowing easier implementation of custom graph optimization problems
Generally faster execution times for large-scale optimization problems
Better support for 3D vision and robotics applications

Cons of g2o

Steeper learning curve and more complex API compared to GTSAM
Less comprehensive documentation and tutorials
Smaller community and fewer maintained examples

Code Comparison

g2o:

g2o::SparseOptimizer optimizer;
g2o::BlockSolver_6_3::LinearSolverType* linearSolver;
linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolver_6_3::PoseMatrixType>();
g2o::BlockSolver_6_3* solver_ptr = new g2o::BlockSolver_6_3(linearSolver);
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);

GTSAM:

gtsam::NonlinearFactorGraph graph;
gtsam::Values initialEstimate;
gtsam::LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
gtsam::Values result = optimizer.optimize();

Both libraries offer powerful optimization capabilities, but g2o provides more flexibility at the cost of complexity, while GTSAM offers a more straightforward API with extensive documentation.

Sophus

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C++ implementation of Lie Groups using Eigen.

Pros of Sophus

Lightweight and focused on Lie groups and algebras
Easier to integrate into existing projects due to its simplicity
Header-only library, simplifying build processes

Cons of Sophus

Limited scope compared to GTSAM's comprehensive functionality
Fewer optimization and estimation tools
Smaller community and less extensive documentation

Code Comparison

Sophus example (rotation matrix to quaternion):

Sophus::SO3d R = Sophus::SO3d::exp(Sophus::Vector3d(0.1, 0.2, 0.3));
Eigen::Quaterniond q = R.unit_quaternion();

GTSAM example (rotation matrix to quaternion):

gtsam::Rot3 R = gtsam::Rot3::Expmap(gtsam::Vector3(0.1, 0.2, 0.3));
gtsam::Quaternion q = R.quaternion();

Both libraries provide similar functionality for basic operations, but GTSAM offers a more extensive set of tools for complex robotics and computer vision tasks. Sophus is more focused on efficient Lie group operations, while GTSAM provides a broader range of probabilistic inference and optimization capabilities.

kalibr

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The Kalibr visual-inertial calibration toolbox

Pros of Kalibr

Specialized for camera-IMU calibration and multi-sensor calibration
User-friendly GUI for visualization and parameter tuning
Supports various camera models and sensor configurations

Cons of Kalibr

More limited in scope compared to GTSAM's general-purpose optimization
Less active development and community support
Primarily focused on calibration, lacking broader SLAM capabilities

Code Comparison

Kalibr (Python-based calibration):

import kalibr_common as kc
import kalibr_camera_calibration as kcc

cam = kc.CameraGeometry(geometry_type='pinhole-radtan')
imu = kc.ImuParameters()
calib = kcc.CameraImuCalibration(cam, imu)
calib.optimize()

GTSAM (C++ factor graph optimization):

#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/slam/PriorFactor.h>

gtsam::NonlinearFactorGraph graph;
graph.add(gtsam::PriorFactor<gtsam::Pose3>(1, initialPose, noisePrior));
gtsam::Values result = gtsam::LevenbergMarquardtOptimizer(graph, initial).optimize();

Both repositories serve different primary purposes, with Kalibr focusing on sensor calibration and GTSAM offering a broader range of optimization tools for various robotics and computer vision applications.

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README

README - Georgia Tech Smoothing and Mapping Library

Important Note

As of January 2023, the develop branch is officially in "Pre 4.3" mode. We envision several API-breaking changes as we switch to C++17 and away from boost.

In addition, features deprecated in 4.2 will be removed. Please use the stable 4.2 release if you need those features. However, most are easily converted and can be tracked down (in 4.2) by disabling the cmake flag GTSAM_ALLOW_DEPRECATED_SINCE_V42.

What is GTSAM?

GTSAM is a C++ library that implements smoothing and mapping (SAM) in robotics and vision, using Factor Graphs and Bayes Networks as the underlying computing paradigm rather than sparse matrices.

The current support matrix is:

Platform	Compiler	Build Status
Ubuntu 20.04/22.04	gcc/clang
macOS	clang
Windows	MSVC

On top of the C++ library, GTSAM includes wrappers for MATLAB & Python.

Quickstart

In the root library folder execute:

#!bash
mkdir build
cd build
cmake ..
make check (optional, runs unit tests)
make install

Prerequisites:

Boost >= 1.65 (Ubuntu: sudo apt-get install libboost-all-dev)
CMake >= 3.0 (Ubuntu: sudo apt-get install cmake)
A modern compiler, i.e., at least gcc 4.7.3 on Linux.

Optional prerequisites - used automatically if findable by CMake:

Intel Threaded Building Blocks (TBB) (Ubuntu: sudo apt-get install libtbb-dev)
Intel Math Kernel Library (MKL) (Ubuntu: installing using APT)
- See INSTALL.md for more installation information
- Note that MKL may not provide a speedup in all cases. Make sure to benchmark your problem with and without MKL.

GTSAM 4 Compatibility

GTSAM 4 introduces several new features, most notably Expressions and a Python toolbox. It also introduces traits, a C++ technique that allows optimizing with non-GTSAM types. That opens the door to retiring geometric types such as Point2 and Point3 to pure Eigen types, which we also do. A significant change which will not trigger a compile error is that zero-initializing of Point2 and Point3 is deprecated, so please be aware that this might render functions using their default constructor incorrect.

Wrappers

We provide support for MATLAB and Python wrappers for GTSAM. Please refer to the linked documents for more details.

Citation

If you are using GTSAM for academic work, please use the following citation:

@software{gtsam,
  author       = {Frank Dellaert and GTSAM Contributors},
  title        = {borglab/gtsam},
  month        = May,
  year         = 2022,
  publisher    = {Georgia Tech Borg Lab},
  version      = {4.2a8},
  doi          = {10.5281/zenodo.5794541},
  url          = {https://github.com/borglab/gtsam)}}
}

To cite the Factor Graphs for Robot Perception book, please use:

@book{factor_graphs_for_robot_perception,
    author={Frank Dellaert and Michael Kaess},
    year={2017},
    title={Factor Graphs for Robot Perception},
    publisher={Foundations and Trends in Robotics, Vol. 6},
    url={http://www.cs.cmu.edu/~kaess/pub/Dellaert17fnt.pdf}
}

If you are using the IMU preintegration scheme, please cite:

@book{imu_preintegration,
    author={Christian Forster and Luca Carlone and Frank Dellaert and Davide Scaramuzza},
    title={IMU preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation},
    year={2015}
}

The Preintegrated IMU Factor

GTSAM includes a state of the art IMU handling scheme based on

Todd Lupton and Salah Sukkarieh, "Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions", TRO, 28(1):61-76, 2012. [link]

Our implementation improves on this using integration on the manifold, as detailed in

Luca Carlone, Zsolt Kira, Chris Beall, Vadim Indelman, and Frank Dellaert, "Eliminating conditionally independent sets in factor graphs: a unifying perspective based on smart factors", Int. Conf. on Robotics and Automation (ICRA), 2014. [link]
Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza, "IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation", Robotics: Science and Systems (RSS), 2015. [link]

If you are using the factor in academic work, please cite the publications above.

In GTSAM 4 a new and more efficient implementation, based on integrating on the NavState tangent space and detailed in this document, is enabled by default. To switch to the RSS 2015 version, set the flag GTSAM_TANGENT_PREINTEGRATION to OFF.

Additional Information

There is a GTSAM users Google group for general discussion.

Read about important GTSAM-Concepts here. A primer on GTSAM Expressions, which support (superfast) automatic differentiation, can be found on the GTSAM wiki on BitBucket.

See the INSTALL file for more detailed installation instructions.

GTSAM is open source under the BSD license, see the LICENSE and LICENSE.BSD files.

Please see the examples/ directory and the USAGE file for examples on how to use GTSAM.

GTSAM was developed in the lab of Frank Dellaert at the Georgia Institute of Technology, with the help of many contributors over the years, see THANKS.

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