1/* ----------------------------------------------------------------------------
2
3 * GTSAM Copyright 2010, Georgia Tech Research Corporation,
4 * Atlanta, Georgia 30332-0415
5 * All Rights Reserved
6 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
7
8 * See LICENSE for the license information
9
10 * -------------------------------------------------------------------------- */
11
12/**
13 * @file OdometryExample.cpp
14 * @brief Simple robot motion example, with prior and two odometry measurements
15 * @author Frank Dellaert
16 */
17
18/**
19 * Example of a simple 2D localization example
20 * - Robot poses are facing along the X axis (horizontal, to the right in 2D)
21 * - The robot moves 2 meters each step
22 * - We have full odometry between poses
23 */
24
25// We will use Pose2 variables (x, y, theta) to represent the robot positions
26#include <gtsam/geometry/Pose2.h>
27
28// In GTSAM, measurement functions are represented as 'factors'. Several common factors
29// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
30// Here we will use Between factors for the relative motion described by odometry measurements.
31// Also, we will initialize the robot at the origin using a Prior factor.
32#include <gtsam/slam/BetweenFactor.h>
33
34// When the factors are created, we will add them to a Factor Graph. As the factors we are using
35// are nonlinear factors, we will need a Nonlinear Factor Graph.
36#include <gtsam/nonlinear/NonlinearFactorGraph.h>
37
38// Finally, once all of the factors have been added to our factor graph, we will want to
39// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
40// GTSAM includes several nonlinear optimizers to perform this step. Here we will use the
41// Levenberg-Marquardt solver
42#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
43
44// Once the optimized values have been calculated, we can also calculate the marginal covariance
45// of desired variables
46#include <gtsam/nonlinear/Marginals.h>
47
48// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
49// nonlinear functions around an initial linearization point, then solve the linear system
50// to update the linearization point. This happens repeatedly until the solver converges
51// to a consistent set of variable values. This requires us to specify an initial guess
52// for each variable, held in a Values container.
53#include <gtsam/nonlinear/Values.h>
54
55using namespace std;
56using namespace gtsam;
57
58int main(int argc, char** argv) {
59 // Create an empty nonlinear factor graph
60 NonlinearFactorGraph graph;
61
62 // Add a prior on the first pose, setting it to the origin
63 // A prior factor consists of a mean and a noise model (covariance matrix)
64 Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
65 auto priorNoise = noiseModel::Diagonal::Sigmas(sigmas: Vector3(0.3, 0.3, 0.1));
66 graph.addPrior(key: 1, prior: priorMean, model: priorNoise);
67
68 // Add odometry factors
69 Pose2 odometry(2.0, 0.0, 0.0);
70 // For simplicity, we will use the same noise model for each odometry factor
71 auto odometryNoise = noiseModel::Diagonal::Sigmas(sigmas: Vector3(0.2, 0.2, 0.1));
72 // Create odometry (Between) factors between consecutive poses
73 graph.emplace_shared<BetweenFactor<Pose2> >(args: 1, args: 2, args&: odometry, args&: odometryNoise);
74 graph.emplace_shared<BetweenFactor<Pose2> >(args: 2, args: 3, args&: odometry, args&: odometryNoise);
75 graph.print(str: "\nFactor Graph:\n"); // print
76
77 // Create the data structure to hold the initialEstimate estimate to the solution
78 // For illustrative purposes, these have been deliberately set to incorrect values
79 Values initial;
80 initial.insert(j: 1, val: Pose2(0.5, 0.0, 0.2));
81 initial.insert(j: 2, val: Pose2(2.3, 0.1, -0.2));
82 initial.insert(j: 3, val: Pose2(4.1, 0.1, 0.1));
83 initial.print(str: "\nInitial Estimate:\n"); // print
84
85 // optimize using Levenberg-Marquardt optimization
86 Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
87 result.print(str: "Final Result:\n");
88
89 // Calculate and print marginal covariances for all variables
90 cout.precision(prec: 2);
91 Marginals marginals(graph, result);
92 cout << "x1 covariance:\n" << marginals.marginalCovariance(variable: 1) << endl;
93 cout << "x2 covariance:\n" << marginals.marginalCovariance(variable: 2) << endl;
94 cout << "x3 covariance:\n" << marginals.marginalCovariance(variable: 3) << endl;
95
96 return 0;
97}
98