g2opy

g2o Graph Optimization Lib for Ubuntu Linux

If you are on Ubuntu and/or its docker contianer,

pip install g2opy

is all you need to get started.

This g2opy module is built based on

• https://github.com/uoip/g2opy
• https://github.com/RainerKuemmerle/g2o

Intellisense Enhanced

License

MIT license. Currently, additional work is the build configuration on top of uoip/g2opy.

Examples

To run the examples, install numpy if applicable.

pip install numpy

Bundle Adjustmend Example

# https://github.com/RainerKuemmerle/g2o/blob/master/g2o/examples/sba/sba_demo.cpp

import numpy as np
import g2opy as g2o
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
from collections import defaultdict

def showCameraPoses(optim, idx, N):
    for i in range(idx, idx + N):
        T = optim.vertex(i).estimate().matrix()
        trans = T[:3, 3]
        print('camera pose at ', i, ': ', trans)

def calcSSE(optim, idx, wPts):
    sse = 0
    N = wPts.shape[0]
    for i in range(idx, idx + N):
        guessedWpt = optim.vertex(i)
        error = guessedWpt.estimate() - wPts[i-idx]
        sse += np.sum(error ** 2)
    return sse

def showWpts(optim, idx, N):
    for i in range(idx, idx + N):
        T = optim.vertex(i).estimate()
        #T = np.round(T, 0)
        print('guessed wPt at ', i-idx, ': ', T)

def main():
    optimizer = g2o.SparseOptimizer()
    solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
    solver = g2o.OptimizationAlgorithmLevenberg(solver)
    optimizer.set_algorithm(solver)

    f, p = 200, 256
    baseline = 0.3
    K = np.array([[f, 0, p],
                  [0, f, p],
                  [0, 0, 1]])

    wPts = np.array([
        [0, 0, 10],
        [-1, 3, 30],
        [2, 2, 37.2],
    ])

    num_pose = 10
    for i in range(0, int(num_pose/2)):
        pose = g2o.Isometry3d(np.identity(3), [(i-2)*10, 0, 0])
        v_se3 = g2o.VertexSCam()
        v_se3.set_cam(f, f, p, p, baseline)
        v_se3.set_id(i)
        v_se3.set_estimate(pose)
        if i < 2:
            v_se3.set_fixed(True)
        v_se3.set_all() # sets transfrom, projection, dR (angle)
        optimizer.add_vertex(v_se3)

    for i in range(int(num_pose/2), num_pose):
        pose = g2o.Isometry3d(np.identity(3), [0, (i - int(num_pose/2)- 2) * 10, 0])
        v_se3 = g2o.VertexSCam()
        v_se3.set_cam(f, f, p, p, baseline)
        v_se3.set_id(i)
        v_se3.set_estimate(pose)
        v_se3.set_fixed(False)
        v_se3.set_all() # sets transfrom, projection, dR (angle)
        optimizer.add_vertex(v_se3)

    point_id = 0

    for i, wpt in enumerate(wPts):
        guessed_wPt = g2o.VertexSBAPointXYZ()
        guessed_wPt.set_id(num_pose + point_id)
        guessed_wPt.set_marginalized(True)
        guessed_wPt.set_estimate(wpt + np.random.randn(3) )
        optimizer.add_vertex(guessed_wPt)

        for j in range(num_pose):
            z = optimizer.vertex(j).map_point(wpt)
            if i > 1:
                z +=  np.random.randn(3)
            edge = g2o.Edge_XYZ_VSC()
            edge.set_vertex(0, guessed_wPt)
            edge.set_vertex(1, optimizer.vertex(j))
            edge.set_measurement(z)
            edge.set_information(np.identity(3))
            optimizer.add_edge(edge)

        point_id += 1

    sse0 = calcSSE(optimizer, num_pose, wPts)
    print('\nRMSE (inliers only):')
    print('before optimization:', np.sqrt(sse0 / wPts.shape[0]))
    showCameraPoses(optimizer, 0, num_pose)
    showWpts(optimizer, num_pose, 3)


    print('Performing full BA:')
    optimizer.initialize_optimization()
    optimizer.set_verbose(False)
    optimizer.optimize(100)

    sse1 = calcSSE(optimizer, num_pose, wPts)

    print('\nRMSE (inliers only):')
    print('after  optimization:', np.sqrt(sse1 / wPts.shape[0]))
    showCameraPoses(optimizer, 0, num_pose)
    showWpts(optimizer, num_pose, 3)


if __name__ == '__main__':
    main()