1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
|
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file timeLago.cpp
* @brief Time the LAGO initialization method
* @author Richard Roberts
* @date Dec 3, 2010
*/
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/lago.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/linear/Sampler.h>
#include <gtsam/base/timing.h>
#include <iostream>
using namespace std;
using namespace gtsam;
int main(int argc, char *argv[]) {
size_t trials = 1;
// read graph
Values::shared_ptr solution;
NonlinearFactorGraph::shared_ptr g;
string inputFile = findExampleDataFile("w10000");
auto model = noiseModel::Diagonal::Sigmas((Vector(3) << 0.05, 0.05, 5.0 * M_PI / 180.0).finished());
boost::tie(g, solution) = load2D(inputFile, model);
// add noise to create initial estimate
Values initial;
auto noise = noiseModel::Diagonal::Sigmas((Vector(3) << 0.5, 0.5, 15.0 * M_PI / 180.0).finished());
Sampler sampler(noise);
for(const auto& [key,pose]: solution->extract<Pose2>())
initial.insert(key, pose.retract(sampler.sample()));
// Add prior on the pose having index (key) = 0
noiseModel::Diagonal::shared_ptr priorModel = //
noiseModel::Diagonal::Sigmas(Vector3(1e-6, 1e-6, 1e-8));
g->addPrior(0, Pose2(), priorModel);
// LAGO
for (size_t i = 0; i < trials; i++) {
{
gttic_(lago);
gttic_(init);
Values lagoInitial = lago::initialize(*g);
gttoc_(init);
gttic_(refine);
GaussNewtonOptimizer optimizer(*g, lagoInitial);
Values result = optimizer.optimize();
gttoc_(refine);
}
{
gttic_(optimize);
GaussNewtonOptimizer optimizer(*g, initial);
Values result = optimizer.optimize();
}
tictoc_finishedIteration_();
}
tictoc_print_();
return 0;
}
|
