Added python test for Karcher mean

release/4.3a0
Frank Dellaert 2019-04-17 00:39:38 -04:00 committed by Fan Jiang
parent 6c00ff163f
commit b0e4075089
1 changed files with 80 additions and 0 deletions

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"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
KarcherMeanFactor unit tests.
Author: Frank Dellaert
"""
# pylint: disable=invalid-name, no-name-in-module, no-member
import unittest
import gtsam
import numpy as np
from gtsam.utils.test_case import GtsamTestCase
KEY = 0
MODEL = gtsam.noiseModel_Unit.Create(3)
def find_Karcher_mean_Rot3(rotations):
"""Find the Karcher mean of given values."""
# Cost function C(R) = \sum PriorFactor(R_i)::error(R)
# No closed form solution.
graph = gtsam.NonlinearFactorGraph()
for R in rotations:
graph.add(gtsam.PriorFactorRot3(KEY, R, MODEL))
initial = gtsam.Values()
initial.insert(KEY, gtsam.Rot3())
result = gtsam.GaussNewtonOptimizer(graph, initial).optimize()
return result.atRot3(KEY)
# Rot3 version
R = gtsam.Rot3.Expmap(np.array([0.1, 0, 0]))
class TestKarcherMean(GtsamTestCase):
def test_find(self):
# Check that optimizing for Karcher mean (which minimizes Between distance)
# gets correct result.
rotations = {R, R.inverse()}
expected = gtsam.Rot3()
actual = find_Karcher_mean_Rot3(rotations)
self.gtsamAssertEquals(expected, actual)
def test_factor(self):
"""Check that the InnerConstraint factor leaves the mean unchanged."""
# Make a graph with two variables, one between, and one InnerConstraint
# The optimal result should satisfy the between, while moving the other
# variable to make the mean the same as before.
# Mean of R and R' is identity. Let's make a BetweenFactor making R21 =
# R*R*R, i.e. geodesic length is 3 rather than 2.
graph = gtsam.NonlinearFactorGraph()
R12 = R.compose(R.compose(R))
graph.add(gtsam.BetweenFactorRot3(1, 2, R12, MODEL))
keys = gtsam.KeyVector()
keys.push_back(1)
keys.push_back(2)
graph.add(gtsam.KarcherMeanFactorRot3(keys))
initial = gtsam.Values()
initial.insert(1, R.inverse())
initial.insert(2, R)
expected = find_Karcher_mean_Rot3([R, R.inverse()])
result = gtsam.GaussNewtonOptimizer(graph, initial).optimize()
actual = find_Karcher_mean_Rot3(
[result.atRot3(1), result.atRot3(2)])
self.gtsamAssertEquals(expected, actual)
self.gtsamAssertEquals(
R12, result.atRot3(1).between(result.atRot3(2)))
if __name__ == "__main__":
unittest.main()