ROS naviagtion analysis: costmap_2d--InflationLayer

这是costmap_2d的最后一个重要的类了。InflationLayer 本身不存储map数据，所以所谓的地图层的概念，仅仅指的是一种对地图的操作，并不是数据层面的地图。
首先来看一个数据结构，知道有这个东西就行了：

class CellData
{
public:
   CellData(double d, double i, unsigned int x, unsigned int y, unsigned int sx, unsigned int sy) :
      distance_(d), index_(i), x_(x), y_(y), src_x_(sx), src_y_(sy)
  {
  }
  double distance_;//
  unsigned int index_;//this cell在map的一维索引
  unsigned int x_, y_;//this cell在map的x，y二维索引
  unsigned int src_x_, src_y_;//this cell距离最近的障碍物点，这个障碍物cell的x，y二维索引
};
//对应的class CellData的比较操作，用于priority queue操作
inline bool operator<(const CellData &a, const CellData &b)
{
  return a.distance_ > b.distance_;
}

函数onInitialize 配置了动态参数服务的回调函数，然后在最后一行调用了matchSize：

void InflationLayer::matchSize()
{
  boost::unique_lock < boost::recursive_mutex > lock(*inflation_access_);
  costmap_2d::Costmap2D* costmap = layered_costmap_->getCostmap();
  resolution_ = costmap->getResolution();
  cell_inflation_radius_ = cellDistance(inflation_radius_);
  computeCaches();//这个函数通过cell_inflation_radius_计算了两个buffer，这两个二维buffer直接存储了[i，j]的distance 和cost

  unsigned int size_x = costmap->getSizeInCellsX(), size_y = costmap->getSizeInCellsY();
  if (seen_)
    delete[] seen_; seen_size_ = size_x * size_y; seen_ = new bool[seen_size_]; }

函数void InflationLayer::updateBounds 内部基本什么都没做，因此
( double* min_x, double* min_y, double* max_x, double* max_y) 会维持上一层地图调用updateBounds的值。

函数void InflationLayer::onFootprintChanged() 也会去调用computeCaches(); 更新两个两个维度为[cell_inflation_radius_+2][cell_inflation_radius_+2] 的二维buffer：cached_costs_，cached_distances_ 。
接下来的函数，需要重点注意，因为这是关乎inflation 具体是如何操作完成的膨胀的工作：

void InflationLayer::updateCosts(costmap_2d::Costmap2D& master_grid, int min_i, int min_j, int max_i,
                                          int max_j)
{
  boost::unique_lock < boost::recursive_mutex > lock(*inflation_access_);
  if (!enabled_)
    return;

  // make sure the inflation queue is empty at the beginning of the cycle (should always be true)
  ROS_ASSERT_MSG(inflation_queue_.empty(), "The inflation queue 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;
    //这里分配了一个size 和master map 尺寸一样的bool 数组
    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.
  //由于（min_i，min_j，max_i，max_j）是由上一层的地图已经在updateBounds中更新过，而InflationLayer层并未去改变它。因此这里的操作是将传入的boudns，按照机器人的膨胀尺寸，扩张这个bounds
  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);
  //下面的两重循环完成将master map中的LETHAL_OBSTACLE cell的下标index，i，j传给函数inline void InflationLayer::enqueue(unsigned char* grid, unsigned int index, unsigned int mx, unsigned int my,unsigned int src_x, unsigned int src_y)，注意这个函数的参数，因此这里传给enqueue的信息为：将master map中，目标cell索引为（index，mx，my），最近的障碍物索引为（src_x,src_y）的。
  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)
      {
      //enqueue函数首先检查当前cell（i, j）到障碍物cell（i, j）的distance，通过查表即可distanceLookup(mx, my, src_x, src_y);如果距离小于cell_inflation_radius_，则检查cost，通过查表即可costLookup(mx, my, src_x, src_y)，然后用cost更新master map 中当前cell的值。最后将当前的cell 打包成CellData类型，并塞到inflation_queue_
        enqueue(master_array, index, i, j, i, j);
      }
    }
  }
//通过上面的操作，现在inflation_queue_里面全部都是obstacle cell，这些cell被打包成CellData类型，包含了这些cell本身的索引，最近障碍物的索引（即它们本身），距离（0）
  while (!inflation_queue_.empty())
  {
    // get the highest priority cell and pop it off the priority queue
    const CellData& current_cell = inflation_queue_.top();

    unsigned int index = current_cell.index_;
    unsigned int mx = current_cell.x_;
    unsigned int my = current_cell.y_;
    unsigned int sx = current_cell.src_x_;
    unsigned int sy = current_cell.src_y_;

    // pop once we have our cell info
    inflation_queue_.pop();
//上面首先从priority queue中弹出最大distance的cell，再将这个cell的前后左右四个cell都塞进inflation_queue_。由于下面关于enqueue的调用，最后两个参数（sx, sy)没有改变，所以保证了这个索引一定是obstacle cell。由于在 enqueue入口会检查 if (!seen_[index])，这保证了这些cell不会被重复的塞进去。由于这是一个priority queue，因此障碍物的周边点，每次都是离障碍物最远的点被弹出，并被检查，这样保证了这种扩张是朝着离障碍物远的方向进行。
    // attempt to put the neighbors of the current cell onto the queue
    if (mx > 0)
      enqueue(master_array, index - 1, mx - 1, my, sx, sy);
    if (my > 0)
      enqueue(master_array, index - size_x, mx, my - 1, sx, sy);
    if (mx < size_x - 1)
      enqueue(master_array, index + 1, mx + 1, my, sx, sy);
    if (my < size_y - 1)
      enqueue(master_array, index + size_x, mx, my + 1, sx, sy);
  }
}

inline void InflationLayer::enqueue(unsigned char* grid, unsigned int index, unsigned int mx, unsigned int my,
                                            unsigned int src_x, unsigned int src_y)
{
  // set the cost of the cell being inserted
  if (!seen_[index])//避免cell被重复的塞进来
  {
    // 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 queue if it is within the inflation radius of the obstacle point
    if (distance > cell_inflation_radius_)//保证了只处理在cell_inflation_radius_范围内的cell
      return;

    // assign the cost associated with the distance from an obstacle to the cell
    unsigned char cost = costLookup(mx, my, src_x, src_y);
    unsigned char old_cost = grid[index];

    if (old_cost == NO_INFORMATION && cost >= INSCRIBED_INFLATED_OBSTACLE)
      grid[index] = cost;
    else
      grid[index] = std::max(old_cost, cost);
    // push the cell data onto the queue and mark
    seen_[index] = true;
    CellData data(distance, index, mx, my, src_x, src_y);
    inflation_queue_.push(data);
  }
}

理解了上述过程，也就明白了，究竟是怎么实现的膨胀操作。

另外distance 计算costmap，是一个指数衰减关系：

  inline unsigned char computeCost(double distance) const
  {
    unsigned char cost = 0;
    if (distance == 0)
      cost = LETHAL_OBSTACLE;
    else if (distance * resolution_ <= inscribed_radius_)
      cost = INSCRIBED_INFLATED_OBSTACLE;
    else
    {
      // make sure cost falls off by Euclidean distance
      double euclidean_distance = distance * resolution_;
      double factor = exp(-1.0 * weight_ * (euclidean_distance - inscribed_radius_));
      cost = (unsigned char)((INSCRIBED_INFLATED_OBSTACLE - 1) * factor);
    }
    return cost;
  }