机器人算法——costmap膨胀层InflationLaye

如下图是更新地图膨胀

void InflationLayer::updateCosts(costmap_2d::Costmap2D& master_grid, int min_i, int min_j, int max_i, int max_j)
{
  //用指针master_array指向主地图，并获取主地图的尺寸，确认seen_数组被正确设置。
  boost::unique_lock < boost::recursive_mutex > lock(*inflation_access_);
  if (!enabled_ || (cell_inflation_radius_ == 0))
    return;

  // make sure the inflation list is empty at the beginning of the cycle (should always be true)
  ROS_ASSERT_MSG(inflation_cells_.empty(), "The inflation list must be empty at the beginning of inflation");

  unsigned char* master_array = master_grid.getCharMap();
  unsigned int size_x = master_grid.getSizeInCellsX(), size_y = master_grid.getSizeInCellsY();

  if (seen_ == NULL) {
    ROS_WARN("InflationLayer::updateCosts(): seen_ array is NULL");
    seen_size_ = size_x * size_y;
    seen_ = new bool[seen_size_];
  }
  else if (seen_size_ != size_x * size_y)
  {
    ROS_WARN("InflationLayer::updateCosts(): seen_ array size is wrong");
    delete[] seen_;
    seen_size_ = size_x * size_y;
    seen_ = new bool[seen_size_];
  }
  memset(seen_, false, size_x * size_y * sizeof(bool));

  // We need to include in the inflation cells outside the bounding
  // box min_i...max_j, by the amount cell_inflation_radius_.  Cells
  // up to that distance outside the box can still influence the costs
  // stored in cells inside the box.
  //边际膨胀
  min_i -= cell_inflation_radius_;
  min_j -= cell_inflation_radius_;
  max_i += cell_inflation_radius_;
  max_j += cell_inflation_radius_;

  min_i = std::max(0, min_i);
  min_j = std::max(0, min_j);
  max_i = std::min(int(size_x), max_i);
  max_j = std::min(int(size_y), max_j);

  // Inflation list; we append cells to visit in a list associated with its distance to the nearest obstacle
  // We use a map to emulate the priority queue used before, with a notable performance boost

  //接下来遍历bound中的cell，找到cost为LETHAL_OBSTACLE，即障碍物cell，将其以CellData形式放进inflation_cells_[0.0]中，inflation_cells_的定义如下：

  //std::map inflation_cells_;
  //它是一个映射，由浮点数→CellData数组，CellData这个类定义在inflation_layer.h中，
  //是专门用来记录当前cell的索引和与它最近的障碍物的索引的。这个映射以距离障碍物的距离为标准给bound内的cell归类，为膨胀做准备；
  //自然距离为0对应的cell即障碍物cell本身，
  //目前得到的inflation_cells_只包括障碍物本身。

  // Start with lethal obstacles: by definition distance is 0.0
  std::vector<CellData>& obs_bin = inflation_cells_[0.0];
  for (int j = min_j; j < max_j; j++)
  {
    for (int i = min_i; i < max_i; i++)
    {
      int index = master_grid.getIndex(i, j);
      unsigned char cost = master_array[index];
      if (cost == LETHAL_OBSTACLE)
      {
        obs_bin.push_back(CellData(index, i, j, i, j));
      }
    }
  }

  // Process cells by increasing distance; new cells are appended to the corresponding distance bin, so they
  // can overtake previously inserted but farther away cells
  //通过增加距离处理细胞；新的单元格被附加到相应的距离仓中，因此它们
  //可以超越先前插入但距离更远的单元格
  std::map<double, std::vector<CellData> >::iterator bin;
  for (bin = inflation_cells_.begin(); bin != inflation_cells_.end(); ++bin)
  {
    for (int i = 0; i < bin->second.size(); ++i)
    {
      // process all cells at distance dist_bin.first
      const CellData& cell = bin->second[i];

      unsigned int index = cell.index_;

      // ignore if already visited
      if (seen_[index])
      {
        continue;
      }

      seen_[index] = true;

      unsigned int mx = cell.x_;
      unsigned int my = cell.y_;
      unsigned int sx = cell.src_x_;
      unsigned int sy = cell.src_y_;

      // assign the cost associated with the distance from an obstacle to the cell
      unsigned char cost = costLookup(mx, my, sx, sy);
      unsigned char old_cost = master_array[index];
      if (old_cost == NO_INFORMATION && (inflate_unknown_ ? (cost > FREE_SPACE) : (cost >= INSCRIBED_INFLATED_OBSTACLE)))
        master_array[index] = cost;
      else
        master_array[index] = std::max(old_cost, cost);

      // attempt to put the neighbors of the current cell onto the inflation list
      if (mx > 0)
        enqueue(index - 1, mx - 1, my, sx, sy);
      if (my > 0)
        enqueue(index - size_x, mx, my - 1, sx, sy);
      if (mx < size_x - 1)
        enqueue(index + 1, mx + 1, my, sx, sy);
      if (my < size_y - 1)
        enqueue(index + size_x, mx, my + 1, sx, sy);
    }
  }

  inflation_cells_.clear();
}

InflationLayer没有自身的栅格地图要维护，直接在主地图上进行操作，它根据膨胀参数设置用来膨胀的“参考矩阵”，并在主地图上从障碍物出发，不断传播更新，完成对整个地图障碍的膨胀。
不断的从障碍物周围”上下左右“开始膨胀，并且根据参考矩阵去计算不同距离的栅格权重。

 // assign the cost associated with the distance from an obstacle to the cell
      unsigned char cost = costLookup(mx, my, sx, sy);
      unsigned char old_cost = master_array[index];
      if (old_cost == NO_INFORMATION && (inflate_unknown_ ? (cost > FREE_SPACE) : (cost >= INSCRIBED_INFLATED_OBSTACLE)))
        master_array[index] = cost;
      else
        master_array[index] = std::max(old_cost, cost);

unsigned char cost = costLookup(mx, my, sx, sy);为参考矩阵计算， master_array[index] = cost;为当前cell栅格赋值。

 // attempt to put the neighbors of the current cell onto the inflation list
      if (mx > 0)
        enqueue(index - 1, mx - 1, my, sx, sy);
      if (my > 0)
        enqueue(index - size_x, mx, my - 1, sx, sy);
      if (mx < size_x - 1)
        enqueue(index + 1, mx + 1, my, sx, sy);
      if (my < size_y - 1)
        enqueue(index + size_x, mx, my + 1, sx, sy);
    }

这是考察下一个点，上下左右，如果在障碍物半径内，则继续循环到上述的“ unsigned char cost = costLookup(mx, my, sx, sy);为参考矩阵计算， master_array[index] = cost;为当前cell栅格赋值。”

如此循环即可，类似于A*算法的膨胀。

/**
*@brief给定成本图中某个单元格的索引，将其放入等待障碍物膨胀的列表中
*@param grid成本图
*@param index单元格的索引
*@param mx单元格的x坐标（可以根据索引计算，但可以节省存储时间）
*@param my单元格的y坐标（可以根据索引计算，但可以节省存储时间）
*@param src_x障碍点通货膨胀的x指数开始于
*@param src_y障碍点通货膨胀的y指数始于
*/
inline void InflationLayer::enqueue(unsigned int index, unsigned int mx, unsigned int my,
                                    unsigned int src_x, unsigned int src_y)
{
  if (!seen_[index])
  {
    // we compute our distance table one cell further than the inflation radius dictates so we can make the check below
    double distance = distanceLookup(mx, my, src_x, src_y);

    // we only want to put the cell in the list if it is within the inflation radius of the obstacle point
    if (distance > cell_inflation_radius_)
      return;

    // push the cell data onto the inflation list and mark
    inflation_cells_[distance].push_back(CellData(index, mx, my, src_x, src_y));
  }
}

 double distance = distanceLookup(mx, my, src_x, src_y);

使用计算考察点和障碍物的距离，然后判断是否在膨胀半径内，如果是，则保存到nflation_cells_中用于下一次循环中。

Reference

1. 机器人控制—代价地图Costmap的层概述