在g2o的基础上改成ceres优化，高博都写好了其他的部分, 后面改ceres就很简单了. 这块我用的是ceres的自动求导，很方便，就是转化为模板仿函数的时候有点麻烦， 代码部分如下

ceres_type.h : ceres优化核心库的头文件

这个文件写的内容ceres优化的残差块. 把i, j时刻的状态都写成15维的数组, 顺序是r,p,v,bg,ba. 每个元素都是3维的, 所以r 部分涉及到R->r转换, sophus都实现好了.

/** * @file ceres_types.cc * @author Frank Zhang (tanhaozhang@connect.polyu.hk) * @brief* @version 0.1 * @date 2023-08-15 ** @copyright Copyright (c) 2023 **/#ifndef SLAM_IN_AUTO_DRIVING_CH4_CERES_TYPE_H_#define SLAM_IN_AUTO_DRIVING_CH4_CERES_TYPE_H_#include #include "common/eigen_types.h"#include "ceres/ceres.h"#include "thirdparty/sophus/sophus/so3.hpp"#include "ch4/imu_preintegration.h"namespace sad{namespace ceres_optimization{class PreintegrationCostFunctionCore {public: PreintegrationCostFunctionCore(std::shared_ptr<sad::IMUPreintegration> imu_preinit, const Eigen::Vector3d gravaty) : preinit_(imu_preinit), dt_(imu_preinit->dt_), grav_(gravaty) {} template <typename T> bool operator()(const T* const i, const T* const j, T* residual) const {Eigen::Matrix<T, 3, 1> r_i(i[0], i[1], i[2]);Eigen::Matrix<T, 3, 1> r_j(j[0], j[1], j[2]);Eigen::Matrix<T, 3, 1> p_i(i[3], i[4], i[5]);Eigen::Matrix<T, 3, 1> p_j(j[3], j[4], j[5]);Eigen::Matrix<T, 3, 1> v_i(i[6], i[7], i[8]);Eigen::Matrix<T, 3, 1> v_j(j[6], j[7], j[8]);Eigen::Matrix<T, 3, 1> bg(i[9], i[10], i[11]);Eigen::Matrix<T, 3, 1> ba(i[12], i[13], i[14]);Sophus::SO3<double> dR = preinit_->GetDeltaRotation(preinit_->bg_);Eigen::Matrix<double, 3, 1> dvd = preinit_->GetDeltaVelocity(preinit_->bg_, preinit_->ba_);Eigen::Matrix<T, 3, 1> dv(T(dvd.x()), T(dvd.y()), T(dvd.z()));Eigen::Matrix<double, 3, 1> dpd = preinit_->GetDeltaPosition(preinit_->bg_, preinit_->ba_);Eigen::Matrix<T, 3, 1> dp(T(dpd.x()), T(dpd.y()), T(dpd.z()));Sophus::SO3<T, 0> R_i = Sophus::SO3<T, 0>::exp(r_i);Sophus::SO3<T, 0> R_j = Sophus::SO3<T, 0>::exp(r_j);Eigen::Matrix<T, 3, 1> grav(T(grav_.x()), T(grav_.y()), T(grav_.z()));Eigen::Matrix<T, 3, 1> er = (dR.inverse() * R_i.inverse() * R_j).log();Eigen::Matrix<T, 3, 3> RiT = R_i.matrix();Eigen::Matrix<T, 3, 1> ev = RiT * (v_j - v_i - grav * T(dt_)) - dv;Eigen::Matrix<T, 3, 1> ep = RiT * (p_j - p_i - v_i * T(dt_) - grav * T(dt_) * T(dt_) * T(0.5)) - dp;residual[0] = T(er[0]);residual[1] = T(er[1]);residual[2] = T(er[2]);residual[3] = T(ev[0]);residual[4] = T(ev[1]);residual[5] = T(ev[2]);residual[6] = T(ep[0]);residual[7] = T(ep[1]);residual[8] = T(ep[2]);return true;}private:const double dt_;std::shared_ptr<sad::IMUPreintegration> preinit_ = nullptr;const Eigen::Vector3d grav_;};ceres::CostFunction* CreatePreintegrationCostFunction(std::shared_ptr<sad::IMUPreintegration> imu_preinit, const Eigen::Vector3d gravaty) {return new ceres::AutoDiffCostFunction<PreintegrationCostFunctionCore, 9, 15, 15>(new PreintegrationCostFunctionCore(imu_preinit, gravaty));}class BiasCostFunctionCore { public:BiasCostFunctionCore(){}template <typename T>bool operator() (const T* const i, const T* const j, T* residual) const{Eigen::Matrix<T, 3, 1> bg_i(i[9], i[10], i[11]);Eigen::Matrix<T, 3, 1> bg_j(j[9], j[10], j[11]);Eigen::Matrix<T, 3, 1> ba_i(i[12], i[13], i[14]);Eigen::Matrix<T, 3, 1> ba_j(j[12], j[13], j[14]);Eigen::Matrix<T, 3, 1> d_ba = ba_j - ba_i;Eigen::Matrix<T, 3, 1> d_bg = bg_j - bg_i;residual[0] = T(d_ba[0]);residual[1] = T(d_ba[1]);residual[2] = T(d_ba[2]);residual[3] = T(d_bg[0]);residual[4] = T(d_bg[1]);residual[5] = T(d_bg[2]);return true;}};ceres::CostFunction* CreateBiasCostFunction() {return new ceres::AutoDiffCostFunction<BiasCostFunctionCore, 6, 15, 15>(new BiasCostFunctionCore());}class PriorCostFunctionCore {public: PriorCostFunctionCore(const std::shared_ptr<sad::NavStated> prior) : prior_(prior) {} template <typename T> bool operator()(const T* const i, T* residual) const {Eigen::Vector3d prior_r_d = prior_->R_.log();Eigen::Vector3d prior_p_d = prior_->p_;Eigen::Vector3d prior_v_d = prior_->v_;Eigen::Vector3d prior_bg_d = prior_->bg_;Eigen::Vector3d prior_ba_d = prior_->ba_;Eigen::Matrix<double, 15, 1> prior_M;prior_M << prior_r_d, prior_p_d, prior_v_d, prior_bg_d, prior_ba_d;for (int temp = 0; temp < prior_M.size(); temp++){residual[temp] = T(prior_M[temp]) - i[temp];}return true;}private: const std::shared_ptr<sad::NavStated> prior_;};ceres::CostFunction* CreatePriorCostFunction(const std::shared_ptr<sad::NavStated> prior) { return new ceres::AutoDiffCostFunction<PriorCostFunctionCore, 15, 15>(new PriorCostFunctionCore(prior));}class GnssCostFunctionCore {public:GnssCostFunctionCore(const Sophus::SE3d gnss_states) : gnss_states_(gnss_states){}template <typename T>bool operator() (const T* const i, T* residual) const{Eigen::Matrix<T, 3, 1> r_i(i[0], i[1], i[2]);Sophus::SO3<T, 0> R_i = Sophus::SO3<T, 0>::exp(r_i);Eigen::Matrix<T, 3, 1> t_i(i[3], i[4], i[5]);Eigen::Matrix<T, 3, 1> e_r = (gnss_states_.so3().inverse() * R_i).log();Eigen::Matrix<T, 3, 1> e_t = t_i - gnss_states_.translation();residual[0] = T(e_r[0]);residual[1] = T(e_r[1]);residual[2] = T(e_r[2]);residual[3] = T(e_t[0]);residual[4] = T(e_t[1]);residual[5] = T(e_t[2]);return true;}private:const Sophus::SE3d gnss_states_;};static ceres::CostFunction* CreateGnssCostFunction(const Sophus::SE3d gnss_states){return new ceres::AutoDiffCostFunction<GnssCostFunctionCore, 6, 15> (new GnssCostFunctionCore(gnss_states));}class OdomCostFunctionCore {public:OdomCostFunctionCore(const Eigen::Vector3d odom_speed_states) : odom_speed_states_(odom_speed_states) {}template <typename T>bool operator() (const T* const j, T* residual ) const {Eigen::Matrix<T, 3, 1> vj(j[6], j[7], j[8]);residual[0] = T(vj[0] - odom_speed_states_[0]);residual[1] = T(vj[1] - odom_speed_states_[1]);residual[2] = T(vj[2] - odom_speed_states_[2]);return true;}private:const Eigen::Vector3d odom_speed_states_;};static ceres::CostFunction* CreatOdomCostFunction(const Eigen::Vector3d odom_speed_states) {return new ceres::AutoDiffCostFunction<OdomCostFunctionCore, 3, 15> (new OdomCostFunctionCore(odom_speed_states));}} // namespace ceres_optimization} //namespace sad#endif

上面代码分别实现了预积分, bias, 先验, GNSS, odom的残差以及其工厂函数. 不得不说啊, ceres自动求导用起来真简单.

gins_pre_integ.cc: 实现ceres预积分优化

这一部分调用上面头文件构造的工厂函数实现残差计算, ceres优化与更新. 这里只粘贴一下不同的地方

else {LOG_FIRST_N(INFO, 1) << "Using Ceres to Solve!";ceres::Problem problem;Eigen::Vector3d last_r_vec = last_frame_->R_.log();Eigen::Vector3d current_r_vec = this_frame_->R_.log();double last_state[15] = {last_r_vec.x(), last_r_vec.y(), last_r_vec.z(), last_frame_->p_.x(),last_frame_->p_.y(),last_frame_->p_.z(), last_frame_->v_.x(),last_frame_->v_.y(), last_frame_->v_.z(),last_frame_->bg_.x(), last_frame_->bg_.y(), last_frame_->bg_.z(), last_frame_->ba_.x(), last_frame_->ba_.y(), last_frame_->ba_.z()};double current_state[15] = {current_r_vec.x(),current_r_vec.y(),current_r_vec.z(),this_frame_->p_.x(),this_frame_->p_.y(),this_frame_->p_.z(),this_frame_->v_.x(),this_frame_->v_.y(),this_frame_->v_.z(),this_frame_->bg_.x(), this_frame_->bg_.y(), this_frame_->bg_.z(),this_frame_->ba_.x(), this_frame_->ba_.y(), this_frame_->ba_.z()};//预积分problem.AddResidualBlock(ceres_optimization::CreatePreintegrationCostFunction(pre_integ_, options_.gravity_), nullptr, last_state, current_state);// 两个零偏problem.AddResidualBlock(ceres_optimization::CreateBiasCostFunction(), nullptr, last_state, current_state);//GNSSproblem.AddResidualBlock(ceres_optimization::CreateGnssCostFunction(last_gnss_.utm_pose_), nullptr, last_state);problem.AddResidualBlock(ceres_optimization::CreateGnssCostFunction(this_gnss_.utm_pose_), nullptr, current_state);//先验problem.AddResidualBlock(ceres_optimization::CreatePriorCostFunction(last_frame_), nullptr, last_state);//ODOMVec3d vel_world = Vec3d::Zero();Vec3d vel_odom = Vec3d::Zero();if (last_odom_set_) {// velocity obsdouble velo_l = options_.wheel_radius_ * last_odom_.left_pulse_ /options_.circle_pulse_ * 2 * M_PI /options_.odom_span_;double velo_r = options_.wheel_radius_ * last_odom_.right_pulse_ /options_.circle_pulse_ * 2 * M_PI /options_.odom_span_;double average_vel = 0.5 * (velo_l + velo_r);vel_odom = Vec3d(average_vel, 0.0, 0.0);vel_world = this_frame_->R_ * vel_odom;problem.AddResidualBlock(ceres_optimization::CreatOdomCostFunction(vel_world), nullptr, current_state);// 重置odom数据到达标志位，等待最新的odom数据last_odom_set_ = false;}ceres::Solver::Options options;options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;options.max_num_iterations = 20;options.num_threads = 4;options.minimizer_progress_to_stdout = false;ceres::Solver::Summary summary;ceres::Solve(options, &problem, &summary);Eigen::Vector3d last_r(last_state[0], last_state[1], last_state[2]);last_frame_->R_ = Sophus::SO3d::exp(last_r);Eigen::Vector3d last_t(last_state[3], last_state[4], last_state[5]);last_frame_->p_ = last_t;Eigen::Vector3d last_v(last_state[6], last_state[7], last_state[8]);last_frame_->v_ = last_v;Eigen::Vector3d last_bg(last_state[9], last_state[10], last_state[11]);last_frame_->bg_ = last_bg;Eigen::Vector3d last_ba(last_state[12], last_state[13], last_state[14]);last_frame_->ba_ = last_ba;Eigen::Vector3d current_r(current_state[0], current_state[1], current_state[2]);this_frame_->R_ = Sophus::SO3d::exp(current_r);Eigen::Vector3d current_t(current_state[3], current_state[4], current_state[5]);this_frame_->p_ = current_t;Eigen::Vector3d current_v(current_state[6], current_state[7], current_state[8]);this_frame_->v_ = current_v;Eigen::Vector3d current_bg(current_state[9], current_state[10], current_state[11]);this_frame_->bg_ = current_bg;Eigen::Vector3d current_ba(current_state[12], current_state[13], current_state[14]);this_frame_->ba_ = current_ba;}// 重置integoptions_.preinteg_options_.init_bg_ = this_frame_->bg_;options_.preinteg_options_.init_ba_ = this_frame_->ba_;pre_integ_ = std::make_shared<IMUPreintegration>(options_.preinteg_options_);}

上面部分代码为了使用autodiff做了好多不必要的数据处理, 如果有更好的解题思路欢迎留言.
上面代码分为以下几步: 1. 初始处理 2. 添加残差 3. ceres优化 4. 更新
效果如下

感觉还行, 没有评价精度

问题

没有评价精度是不是比g2o好一些
没有评价算力是不是比g2o小一些
没有实现解析求导, 正在搞