isam2 优化pose graph
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gtsam里面只有一个isam2的例子,那个例子里面没有添加位姿闭环约束,主要是视觉BA。而通过闭环优化位姿的gtsam程序主要是Pose2SLAMExample.cpp等,这种用法类似g2o,不能体现isam2的增量优化特性,因此我仿照Pose2SLAMExample里的数据写了一个增量优化位姿的isam2程序,用法上还是有isam2的特性,特别注意graph里的只有isam2优化以后新加的约束,具体见代码。
/** * A simple 2D pose slam example * - The robot moves in a 2 meter square * - The robot moves 2 meters each step, turning 90 degrees after each step * - The robot initially faces along the X axis (horizontal, to the right in 2D) * - We have full odometry between pose * - We have a loop closure constraint when the robot returns to the first position * x5 -- x4 * | | * x1 -- x2 -- x3 */// Each variable in the system (poses and landmarks) must be identified with a unique key.// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).// Here we will use Symbols#include <gtsam/inference/Symbol.h>// We want to use iSAM2 to solve the pose optimazation problem incrementally, so// include iSAM2 here#include <gtsam/nonlinear/ISAM2.h>// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,// and initial guesses for any new variables used in the added factors#include <gtsam/nonlinear/NonlinearFactorGraph.h>#include <gtsam/nonlinear/Values.h>#include <vector>// In planar SLAM example we use Pose2 variables (x, y, theta) to represent the robot poses#include <gtsam/geometry/Pose2.h>// We will use simple integer Keys to refer to the robot poses.#include <gtsam/inference/Key.h>// In GTSAM, measurement functions are represented as 'factors'. Several common factors// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.// Here we will use Between factors for the relative motion described by odometry measurements.// We will also use a Between Factor to encode the loop closure constraint// Also, we will initialize the robot at the origin using a Prior factor.#include <gtsam/slam/PriorFactor.h>#include <gtsam/slam/BetweenFactor.h>// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the// nonlinear functions around an initial linearization point, then solve the linear system// to update the linearization point. This happens repeatedly until the solver converges// to a consistent set of variable values. This requires us to specify an initial guess// for each variable, held in a Values container.#include <gtsam/nonlinear/Values.h>using namespace std;using namespace gtsam;int main(){ // 向量保存好模拟的位姿和测量,到时候一个个往isam2里填加 std::vector< BetweenFactor<Pose2> > gra; std::vector< Pose2 > initPose; // For simplicity, we will use the same noise model for odometry and loop closures noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1)); gra.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2, 0, 0 ), model)); gra.push_back(BetweenFactor<Pose2>(2, 3, Pose2(2, 0, M_PI_2), model)); gra.push_back(BetweenFactor<Pose2>(3, 4, Pose2(2, 0, M_PI_2), model)); gra.push_back(BetweenFactor<Pose2>(4, 5, Pose2(2, 0, M_PI_2), model)); gra.push_back(BetweenFactor<Pose2>(5, 2, Pose2(2, 0, M_PI_2), model)); initPose.push_back(Pose2(0.5, 0.0, 0.2 )); initPose.push_back( Pose2(2.3, 0.1, -0.2 )); initPose.push_back( Pose2(4.1, 0.1, M_PI_2)); initPose.push_back( Pose2(4.0, 2.0, M_PI )); initPose.push_back( Pose2(2.1, 2.1, -M_PI_2)); // Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps to maintain proper linearization // and efficient variable ordering, iSAM2 performs partial relinearization/reordering at each step. A parameter // structure is available that allows the user to set various properties, such as the relinearization threshold // and type of linear solver. For this example, we we set the relinearization threshold small so the iSAM2 result // will approach the batch result. ISAM2Params parameters; parameters.relinearizeThreshold = 0.01; parameters.relinearizeSkip = 1; ISAM2 isam(parameters); // Create a Factor Graph and Values to hold the new data // 注意isam2的graph里只添加isam2更新状态以后新测量到的约束 NonlinearFactorGraph graph; Values initialEstimate; // the first pose don't need to update for( int i =0; i<5 ;i++) { // Add an initial guess for the current pose initialEstimate.insert(i+1, initPose[i]); if(i == 0) { // Add a prior on the first pose, setting it to the origin // A prior factor consists of a mean and a noise model (covariance matrix) noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1)); graph.push_back(PriorFactor<Pose2>(1, Pose2(0, 0, 0), priorNoise)); }else { graph.push_back(gra[i-1]); // ie: when i = 1 , robot at pos2, there is a edge gra[0] between pos1 and pos2 if(i == 4) { graph.push_back(gra[4]); // when robot at pos5, there two edge, one is pos4 ->pos5, another is pos5->pos2 (grad[4]) } isam.update(graph, initialEstimate); isam.update(); Values currentEstimate = isam.calculateEstimate(); cout << "****************************************************" << endl; cout << "Frame " << i << ": " << endl; currentEstimate.print("Current estimate: "); // Clear the factor graph and values for the next iteration // 特别重要,update以后,清空原来的约束。已经加入到isam2的那些会用bayes tree保管,你不用操心了。 graph.resize(0); initialEstimate.clear(); } } return 0; }
这个程序的优化结果和Pose2SLAMExample.cpp几乎一样,但是使用的是增量优化的方式,有其独特的优点。
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