slambook2

Pros of ORB_SLAM2

• More focused on a specific SLAM implementation (ORB-SLAM2)
• Better performance and robustness in real-world scenarios
• More extensive documentation and community support

Cons of ORB_SLAM2

• Less educational content compared to slambook2
• More complex codebase, potentially harder for beginners to understand
• Limited to visual SLAM, while slambook2 covers a broader range of SLAM topics

Code Comparison

ORB_SLAM2 (feature extraction):

void Frame::ExtractORB(int flag, const cv::Mat &im)
{
    if(flag==0)
        (*mpORBextractorLeft)(im,cv::Mat(),mvKeys,mDescriptors);
    else
        (*mpORBextractorRight)(im,cv::Mat(),mvKeysRight,mDescriptorsRight);
}

slambook2 (feature extraction):

void Frame::ExtractORB() {
    cv::Ptr<cv::Feature2D> orb = cv::ORB::create(500);
    orb->detect(image_, keypoints_);
    orb->compute(image_, keypoints_, descriptors_);
}

The code snippets show different approaches to feature extraction. ORB_SLAM2 uses a more customized implementation, while slambook2 relies on OpenCV's built-in ORB feature extractor.

Pros of LSD-SLAM

• LSD-SLAM is a real-time monocular SLAM system that can operate on a wide range of hardware, including mobile devices.
• The algorithm is robust to illumination changes and can handle dynamic scenes.
• LSD-SLAM provides a dense 3D reconstruction of the environment, which can be useful for various applications.

Cons of LSD-SLAM

• LSD-SLAM is a more complex system compared to slambook2, with a steeper learning curve.
• The documentation and community support for LSD-SLAM may not be as extensive as slambook2.
• LSD-SLAM may have higher computational requirements, making it less suitable for resource-constrained devices.

Code Comparison

slambook2:

// Compute the Jacobian
Eigen::Matrix<double, 2, 6> jacobian = Eigen::Matrix<double, 2, 6>::Zero();
jacobian(0, 0) = 1;
jacobian(0, 2) = -u;
jacobian(0, 3) = -u*v;
jacobian(0, 4) = (1+u*u);
jacobian(0, 5) = -v;
jacobian(1, 1) = 1;
jacobian(1, 2) = -v;
jacobian(1, 3) = -(1+v*v);
jacobian(1, 4) = u*v;
jacobian(1, 5) = u;

LSD-SLAM:

// Compute the Jacobian
Eigen::Matrix<float, 2, 6> jacobian;
jacobian(0, 0) = 1.0f;
jacobian(0, 2) = -u;
jacobian(0, 3) = -u*v;
jacobian(0, 4) = (1+u*u);
jacobian(0, 5) = -v;
jacobian(1, 1) = 1.0f;
jacobian(1, 2) = -v;
jacobian(1, 3) = -(1+v*v);
jacobian(1, 4) = u*v;
jacobian(1, 5) = u;

The code for computing the Jacobian matrix is similar in both projects, with the main difference being the use of double precision in slambook2 and float precision in LSD-SLAM.

Pros of g2o

• More mature and widely used in the SLAM community
• Offers a broader range of optimization algorithms and solvers
• Better documentation and community support

Cons of g2o

• Steeper learning curve for beginners
• Less focus on educational aspects compared to slambook2
• May be overkill for simpler SLAM projects

Code Comparison

g2o example (vertex addition):

g2o::VertexSE3* v = new g2o::VertexSE3();
v->setId(vertex_id);
v->setEstimate(Sophus::SE3d());
optimizer.addVertex(v);

slambook2 example (vertex addition):

VertexPose *vertex_pose = new VertexPose();
vertex_pose->setId(vertex_id);
vertex_pose->setEstimate(Sophus::SE3d());
optimizer.addVertex(vertex_pose);

Both repositories provide similar functionality for adding vertices to the optimization graph, but g2o offers more specialized vertex types and a wider range of options for different SLAM scenarios.

Pros of Ceres Solver

• More mature and widely used in industry and research
• Highly optimized for large-scale problems
• Extensive documentation and community support

Cons of Ceres Solver

• Steeper learning curve for beginners
• Less focused on SLAM-specific applications
• Requires more setup and configuration

Code Comparison

Ceres Solver example:

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

Slambook2 example:

g2o::SparseOptimizer optimizer;
optimizer.setAlgorithm(new g2o::OptimizationAlgorithmLevenberg);
g2o::VertexSE3Expmap *pose = new g2o::VertexSE3Expmap();
optimizer.addVertex(pose);
optimizer.initializeOptimization();
optimizer.optimize(10);

Summary

Ceres Solver is a more general-purpose optimization library with broader applications, while Slambook2 is specifically tailored for SLAM (Simultaneous Localization and Mapping) problems. Ceres Solver offers more flexibility and performance for large-scale problems, but may be more challenging for beginners. Slambook2 provides a more accessible entry point for those focusing on SLAM applications, with examples and tutorials specific to this domain.

Pros of kalibr

• Specialized tool for sensor calibration, particularly for multi-sensor systems
• Supports a wide range of sensor types, including cameras, IMUs, and LiDARs
• Provides detailed documentation and examples for various calibration scenarios

Cons of kalibr

• Narrower focus compared to slambook2, which covers a broader range of SLAM topics
• Steeper learning curve for users not specifically focused on calibration tasks
• Less comprehensive coverage of general SLAM concepts and algorithms

Code Comparison

kalibr (Python):

import kalibr_common as kc
import kalibr_camera_calibration as kcc

cam = kc.ConfigReader.camera_from_file(cam_yaml)
imu = kc.ConfigReader.imu_from_file(imu_yaml)

slambook2 (C++):

#include <myslam/visual_odometry.h>

auto vo = std::make_shared<myslam::VisualOdometry>();
vo->SetCameraIntrinsics(fx, fy, cx, cy);
vo->AddFrame(frame);

While kalibr focuses on sensor calibration with specialized tools and configurations, slambook2 provides a broader introduction to SLAM concepts and implementations. The code snippets reflect their different focuses, with kalibr emphasizing configuration and calibration, and slambook2 demonstrating core SLAM functionality.