kalibr

Pros of GTSAM

• More comprehensive library for factor graph optimization and SLAM
• Supports a wider range of applications beyond camera calibration
• Active development with frequent updates and contributions

Cons of GTSAM

• Steeper learning curve due to its broader scope
• May be overkill for simple camera calibration tasks
• Requires more setup and configuration for specific use cases

Code Comparison

Kalibr (Python):

import kalibr_common as kc
import kalibr_camera_calibration as kcc

calibrator = kcc.CameraCalibrator()
calibrator.loadData(bag_file)
calibrator.calibrate()

GTSAM (C++):

#include <gtsam/geometry/Cal3_S2.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>

gtsam::NonlinearFactorGraph graph;
gtsam::Cal3_S2::shared_ptr K(new gtsam::Cal3_S2(fx, fy, s, u0, v0));
graph.add(gtsam::PriorFactor<gtsam::Cal3_S2>(1, *K, noise_model));

Summary

GTSAM offers a more versatile toolset for various optimization problems, including SLAM and sensor fusion, while Kalibr focuses specifically on camera and IMU calibration. GTSAM's flexibility comes at the cost of increased complexity, whereas Kalibr provides a more streamlined experience for its targeted use case.

Pros of Ceres Solver

• More general-purpose optimization library, suitable for a wide range of problems
• Highly optimized and efficient, with support for large-scale problems
• Extensive documentation and active community support

Cons of Ceres Solver

• Steeper learning curve for beginners in optimization
• Requires more manual setup and configuration for specific problems

Code Comparison

Kalibr (Python-based camera calibration):

import kalibr_common as kc
import kalibr_camera_calibration as kcc

calibrator = kcc.CameraCalibrator()
calibrator.loadData(bag_file)
calibrator.calibrate()

Ceres Solver (C++ optimization problem):

#include "ceres/ceres.h"

ceres::Problem problem;
ceres::Solver::Options options;
ceres::Solver::Summary summary;

problem.AddResidualBlock(/* ... */);
ceres::Solve(options, &problem, &summary);

Kalibr is more focused on camera calibration tasks, providing a higher-level interface for this specific application. Ceres Solver offers a lower-level, more flexible framework for general optimization problems, requiring more explicit problem formulation but allowing for a broader range of applications.

Pros of g2o

• More general-purpose optimization framework, suitable for a wider range of problems
• Highly efficient implementation, particularly for large-scale optimization
• Extensive documentation and examples available

Cons of g2o

• Steeper learning curve due to its more complex architecture
• Less focused on camera and IMU calibration tasks specifically
• Requires more setup and configuration for specific use cases

Code Comparison

g2o example (graph optimization):

g2o::SparseOptimizer optimizer;
optimizer.setAlgorithm(new g2o::OptimizationAlgorithmLevenberg);

g2o::VertexSE3* v = new g2o::VertexSE3;
v->setEstimate(Eigen::Isometry3d::Identity());
optimizer.addVertex(v);

Kalibr example (camera-IMU calibration):

kalibr_calibrate_imu_camera --target april_6x6.yaml \
    --cam camchain.yaml \
    --imu imu.yaml \
    --bag calibration_data.bag \
    --output-folder output_calibration

Pros of OpenCV

• Broader scope: Comprehensive computer vision library with a wide range of functionalities
• Larger community: More extensive user base, documentation, and third-party resources
• Multi-language support: Bindings for Python, Java, and other languages

Cons of OpenCV

• Less specialized: Not focused on camera calibration like Kalibr
• Steeper learning curve: Due to its extensive feature set, it can be overwhelming for beginners
• Heavier resource usage: Larger library size and potentially higher memory footprint

Code Comparison

Kalibr (Python):

import kalibr_common as kc
import kalibr_camera_calibration as kcc

calibrator = kcc.CameraCalibration()
calibrator.loadData(bag_file)
calibrator.calibrate()

OpenCV (Python):

import cv2
import numpy as np

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

Both libraries offer camera calibration functionality, but Kalibr is more specialized for this task, while OpenCV provides a broader range of computer vision tools. Kalibr's approach is more automated and tailored for ROS environments, whereas OpenCV's method requires more manual setup but offers greater flexibility for various calibration scenarios.

Pros of zlib

• Widely used and battle-tested compression library
• Lightweight and efficient implementation
• Extensive cross-platform support

Cons of zlib

• Limited to compression/decompression functionality
• Less active development compared to Kalibr
• Fewer advanced features for specialized use cases

Code Comparison

zlib (compression example):

z_stream strm;
deflateInit(&strm, Z_DEFAULT_COMPRESSION);
deflate(&strm, Z_FINISH);
deflateEnd(&strm);

Kalibr (camera calibration example):

import kalibr
calibrator = kalibr.EurocCalibrator()
calibrator.loadDataset(dataset_path)
calibrator.calibrate()
calibrator.saveResults(output_path)

Summary

zlib is a focused, widely-adopted compression library, while Kalibr is a specialized toolbox for sensor calibration in robotics. zlib offers simplicity and broad compatibility, but Kalibr provides more advanced features for its specific domain. The code examples highlight their different purposes: zlib for data compression and Kalibr for complex calibration tasks.

Pros of Sophus

• Lightweight and focused on Lie groups and algebras
• Easier to integrate into existing C++ projects
• More actively maintained with recent updates

Cons of Sophus

• Limited scope compared to Kalibr's comprehensive calibration tools
• Lacks GUI and visualization features
• May require additional libraries for full functionality in robotics applications

Code Comparison

Sophus example (transformation composition):

Sophus::SE3d T1, T2;
Sophus::SE3d T_composed = T1 * T2;

Kalibr example (camera calibration):

import kalibr
calibrator = kalibr.CameraCalibrator()
calibrator.loadImages(image_folder)
calibrator.calibrate()

Summary

Sophus is a specialized library for Lie group operations, while Kalibr is a comprehensive toolkit for sensor calibration. Sophus is more suitable for projects requiring efficient geometric computations, whereas Kalibr excels in multi-sensor calibration tasks. The choice between them depends on the specific requirements of your robotics or computer vision project.