Program Listing for File smoother.hpp

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// Copyright (c) 2021 RoboTech Vision
// Copyright (c) 2020, Samsung Research America
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License. Reserved.

#ifndef NAV2_CONSTRAINED_SMOOTHER__SMOOTHER_HPP_
#define NAV2_CONSTRAINED_SMOOTHER__SMOOTHER_HPP_

#include <cmath>
#include <vector>
#include <iostream>
#include <memory>
#include <queue>
#include <utility>
#include <deque>
#include <limits>
#include <algorithm>

#include "nav2_constrained_smoother/smoother_cost_function.hpp"
#include "nav2_constrained_smoother/utils.hpp"

#include "ceres/ceres.h"
#include "Eigen/Core"

namespace nav2_constrained_smoother
{

class Smoother
{
public:
  Smoother() {}

  ~Smoother() {}

  void initialize(const OptimizerParams params)
  {
    debug_ = params.debug;

    options_.linear_solver_type = params.solver_types.at(params.linear_solver_type);

    options_.max_num_iterations = params.max_iterations;

    options_.function_tolerance = params.fn_tol;
    options_.gradient_tolerance = params.gradient_tol;
    options_.parameter_tolerance = params.param_tol;

    if (debug_) {
      options_.minimizer_progress_to_stdout = true;
      options_.logging_type = ceres::LoggingType::PER_MINIMIZER_ITERATION;
    } else {
      options_.logging_type = ceres::SILENT;
    }
  }

  bool smooth(
    std::vector<Eigen::Vector3d> & path,
    const Eigen::Vector2d & start_dir,
    const Eigen::Vector2d & end_dir,
    const nav2_costmap_2d::Costmap2D * costmap,
    const SmootherParams & params)
  {
    // Path has always at least 2 points
    if (path.size() < 2) {
      throw std::runtime_error("Constrained smoother: Path must have at least 2 points");
    }

    options_.max_solver_time_in_seconds = params.max_time;

    ceres::Problem problem;
    std::vector<Eigen::Vector3d> path_optim;
    std::vector<bool> optimized;
    if (buildProblem(path, costmap, params, problem, path_optim, optimized)) {
      // solve the problem
      ceres::Solver::Summary summary;
      ceres::Solve(options_, &problem, &summary);
      if (debug_) {
        RCLCPP_INFO(rclcpp::get_logger("smoother_server"), "%s", summary.FullReport().c_str());
      }
      if (!summary.IsSolutionUsable() || summary.initial_cost - summary.final_cost < 0.0) {
        return false;
      }
    } else {
      RCLCPP_INFO(rclcpp::get_logger("smoother_server"), "Path too short to optimize");
    }

    upsampleAndPopulate(path_optim, optimized, start_dir, end_dir, params, path);

    return true;
  }

private:
  bool buildProblem(
    const std::vector<Eigen::Vector3d> & path,
    const nav2_costmap_2d::Costmap2D * costmap,
    const SmootherParams & params,
    ceres::Problem & problem,
    std::vector<Eigen::Vector3d> & path_optim,
    std::vector<bool> & optimized)
  {
    // Create costmap grid
    costmap_grid_ = std::make_shared<ceres::Grid2D<u_char>>(
      costmap->getCharMap(), 0, costmap->getSizeInCellsY(), 0, costmap->getSizeInCellsX());
    auto costmap_interpolator = std::make_shared<ceres::BiCubicInterpolator<ceres::Grid2D<u_char>>>(
      *costmap_grid_);

    // Create residual blocks
    const double cusp_half_length = params.cusp_zone_length / 2;
    ceres::LossFunction * loss_function = NULL;
    path_optim = path;
    optimized = std::vector<bool>(path.size());
    optimized[0] = true;
    int prelast_i = -1;
    int last_i = 0;
    double last_direction = path_optim[0][2];
    bool last_was_cusp = false;
    bool last_is_reversing = false;
    std::deque<std::pair<double, SmootherCostFunction *>> potential_cusp_funcs;
    double last_segment_len = EPSILON;
    double potential_cusp_funcs_len = 0;
    double len_since_cusp = std::numeric_limits<double>::infinity();

    for (size_t i = 1; i < path_optim.size(); i++) {
      auto & pt = path_optim[i];
      bool is_cusp = false;
      if (i != path_optim.size() - 1) {
        is_cusp = pt[2] * last_direction < 0;
        last_direction = pt[2];

        // skip to downsample if can be skipped (no forward/reverse direction change)
        if (!is_cusp &&
          i > (params.keep_start_orientation ? 1 : 0) &&
          i < path_optim.size() - (params.keep_goal_orientation ? 2 : 1) &&
          static_cast<int>(i - last_i) < params.path_downsampling_factor)
        {
          continue;
        }
      }

      // keep distance inequalities between poses
      // (some might have been downsampled while others might not)
      double current_segment_len = (path_optim[i] - path_optim[last_i]).block<2, 1>(0, 0).norm();

      // forget cost functions which don't have chance to be part of a cusp zone
      potential_cusp_funcs_len += current_segment_len;
      while (!potential_cusp_funcs.empty() && potential_cusp_funcs_len > cusp_half_length) {
        potential_cusp_funcs_len -= potential_cusp_funcs.front().first;
        potential_cusp_funcs.pop_front();
      }

      // update cusp zone costmap weights
      if (is_cusp) {
        double len_to_cusp = current_segment_len;
        for (int i = potential_cusp_funcs.size() - 1; i >= 0; i--) {
          auto & f = potential_cusp_funcs[i];
          double new_weight =
            params.cusp_costmap_weight * (1.0 - len_to_cusp / cusp_half_length) +
            params.costmap_weight * len_to_cusp / cusp_half_length;
          if (std::abs(new_weight - params.cusp_costmap_weight) <
            std::abs(f.second->getCostmapWeight() - params.cusp_costmap_weight))
          {
            f.second->setCostmapWeight(new_weight);
          }
          len_to_cusp += f.first;
        }
        potential_cusp_funcs_len = 0;
        potential_cusp_funcs.clear();
        len_since_cusp = 0;
      }

      // add cost function
      optimized[i] = true;
      if (prelast_i != -1) {
        double costmap_weight = params.costmap_weight;
        if (len_since_cusp <= cusp_half_length) {
          costmap_weight =
            params.cusp_costmap_weight * (1.0 - len_since_cusp / cusp_half_length) +
            params.costmap_weight * len_since_cusp / cusp_half_length;
        }
        SmootherCostFunction * cost_function = new SmootherCostFunction(
          path[last_i].template block<2, 1>(
            0,
            0),
          (last_was_cusp ? -1 : 1) * last_segment_len / current_segment_len,
          last_is_reversing,
          costmap,
          costmap_interpolator,
          params,
          costmap_weight
        );
        problem.AddResidualBlock(
          cost_function->AutoDiff(), loss_function,
          path_optim[last_i].data(), pt.data(), path_optim[prelast_i].data());

        potential_cusp_funcs.emplace_back(current_segment_len, cost_function);
      }

      // shift current to last and last to pre-last
      last_was_cusp = is_cusp;
      last_is_reversing = last_direction < 0;
      prelast_i = last_i;
      last_i = i;
      len_since_cusp += current_segment_len;
      last_segment_len = std::max(EPSILON, current_segment_len);
    }

    int posesToOptimize = problem.NumParameterBlocks() - 2;  // minus start and goal
    if (params.keep_goal_orientation) {
      posesToOptimize -= 1;  // minus goal orientation holder
    }
    if (params.keep_start_orientation) {
      posesToOptimize -= 1;  // minus start orientation holder
    }
    if (posesToOptimize <= 0) {
      return false;  // nothing to optimize
    }
    // first two and last two points are constant (to keep start and end direction)
    problem.SetParameterBlockConstant(path_optim.front().data());
    if (params.keep_start_orientation) {
      problem.SetParameterBlockConstant(path_optim[1].data());
    }
    if (params.keep_goal_orientation) {
      problem.SetParameterBlockConstant(path_optim[path_optim.size() - 2].data());
    }
    problem.SetParameterBlockConstant(path_optim.back().data());
    return true;
  }

  void upsampleAndPopulate(
    const std::vector<Eigen::Vector3d> & path_optim,
    const std::vector<bool> & optimized,
    const Eigen::Vector2d & start_dir,
    const Eigen::Vector2d & end_dir,
    const SmootherParams & params,
    std::vector<Eigen::Vector3d> & path)
  {
    // Populate path, assign orientations, interpolate skipped/upsampled poses
    path.clear();
    if (params.path_upsampling_factor > 1) {
      path.reserve(params.path_upsampling_factor * (path_optim.size() - 1) + 1);
    }
    int last_i = 0;
    int prelast_i = -1;
    Eigen::Vector2d prelast_dir;
    for (int i = 1; i <= static_cast<int>(path_optim.size()); i++) {
      if (i == static_cast<int>(path_optim.size()) || optimized[i]) {
        if (prelast_i != -1) {
          Eigen::Vector2d last_dir;
          auto & prelast = path_optim[prelast_i];
          auto & last = path_optim[last_i];

          // Compute orientation of last
          if (i < static_cast<int>(path_optim.size())) {
            auto & current = path_optim[i];
            Eigen::Vector2d tangent_dir = tangentDir<double>(
              prelast.block<2, 1>(0, 0),
              last.block<2, 1>(0, 0),
              current.block<2, 1>(0, 0),
              prelast[2] * last[2] < 0);

            last_dir =
              tangent_dir.dot((current - last).block<2, 1>(0, 0) * last[2]) >= 0 ?
              tangent_dir :
              -tangent_dir;
            last_dir.normalize();
          } else if (params.keep_goal_orientation) {
            last_dir = end_dir;
          } else {
            last_dir = (last - prelast).block<2, 1>(0, 0) * last[2];
            last_dir.normalize();
          }
          double last_angle = atan2(last_dir[1], last_dir[0]);

          // Interpolate poses between prelast and last
          int interp_cnt = (last_i - prelast_i) * params.path_upsampling_factor - 1;
          if (interp_cnt > 0) {
            Eigen::Vector2d last_pt = last.block<2, 1>(0, 0);
            Eigen::Vector2d prelast_pt = prelast.block<2, 1>(0, 0);
            double dist = (last_pt - prelast_pt).norm();
            Eigen::Vector2d pt1 = prelast_pt + prelast_dir * dist * 0.4 * prelast[2];
            Eigen::Vector2d pt2 = last_pt - last_dir * dist * 0.4 * prelast[2];
            for (int j = 1; j <= interp_cnt; j++) {
              double interp = j / static_cast<double>(interp_cnt + 1);
              Eigen::Vector2d pt = cubicBezier(prelast_pt, pt1, pt2, last_pt, interp);
              path.emplace_back(pt[0], pt[1], 0.0);
            }
          }
          path.emplace_back(last[0], last[1], last_angle);

          // Assign orientations to interpolated points
          for (size_t j = path.size() - 1 - interp_cnt; j < path.size() - 1; j++) {
            Eigen::Vector2d tangent_dir = tangentDir<double>(
              path[j - 1].block<2, 1>(0, 0),
              path[j].block<2, 1>(0, 0),
              path[j + 1].block<2, 1>(0, 0),
              false);
            tangent_dir =
              tangent_dir.dot((path[j + 1] - path[j]).block<2, 1>(0, 0) * prelast[2]) >= 0 ?
              tangent_dir :
              -tangent_dir;
            path[j][2] = atan2(tangent_dir[1], tangent_dir[0]);
          }

          prelast_dir = last_dir;
        } else {  // start pose
          auto & start = path_optim[0];
          Eigen::Vector2d dir = params.keep_start_orientation ?
            start_dir :
            ((path_optim[i] - start).block<2, 1>(0, 0) * start[2]).normalized();
          path.emplace_back(start[0], start[1], atan2(dir[1], dir[0]));
          prelast_dir = dir;
        }
        prelast_i = last_i;
        last_i = i;
      }
    }
  }

  /*
    Piecewise cubic bezier curve as defined by Adobe in Postscript
    The two end points are pt0 and pt3
    Their associated control points are pt1 and pt2
  */
  static Eigen::Vector2d cubicBezier(
    Eigen::Vector2d & pt0, Eigen::Vector2d & pt1,
    Eigen::Vector2d & pt2, Eigen::Vector2d & pt3, double mu)
  {
    Eigen::Vector2d a, b, c, pt;

    c[0] = 3 * (pt1[0] - pt0[0]);
    c[1] = 3 * (pt1[1] - pt0[1]);
    b[0] = 3 * (pt2[0] - pt1[0]) - c[0];
    b[1] = 3 * (pt2[1] - pt1[1]) - c[1];
    a[0] = pt3[0] - pt0[0] - c[0] - b[0];
    a[1] = pt3[1] - pt0[1] - c[1] - b[1];

    pt[0] = a[0] * mu * mu * mu + b[0] * mu * mu + c[0] * mu + pt0[0];
    pt[1] = a[1] * mu * mu * mu + b[1] * mu * mu + c[1] * mu + pt0[1];

    return pt;
  }

  bool debug_;
  ceres::Solver::Options options_;
  std::shared_ptr<ceres::Grid2D<u_char>> costmap_grid_;
};

}  // namespace nav2_constrained_smoother

#endif  // NAV2_CONSTRAINED_SMOOTHER__SMOOTHER_HPP_