test_costmap_bbf.cpp

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#include <map>
#include <cmath>

#include <gtest/gtest.h>

#include <planner_cspace/planner_3d/costmap_bbf.h>
#include <planner_cspace/bbf.h>

namespace planner_cspace
{
namespace planner_3d
{
TEST(CostmapBBF, ForEach)
{
  CostmapBBF bbf;
  const int w = 10, h = 5;
  bbf.reset(CostmapBBF::Vec(w, h, 1));
  bool called[w][h];
  for (int i = 0; i < w; i++)
    for (int j = 0; j < h; j++)
      called[i][j] = false;

  const auto cb = [&called, w, h](const CostmapBBF::Vec& p, bbf::BinaryBayesFilter& /* bbf */)
  {
    ASSERT_LT(p[0], w);
    ASSERT_LT(p[1], h);
    ASSERT_GE(p[0], 0);
    ASSERT_GE(p[1], 0);
    ASSERT_EQ(0, p[2]);
    called[p[0]][p[1]] = true;
  };
  bbf.forEach(cb);

  int cnt_called = 0;
  for (int i = 0; i < w; i++)
    for (int j = 0; j < h; j++)
      if (called[i][j])
        cnt_called++;

  ASSERT_EQ(w * h, cnt_called);
}
TEST(CostmapBBF, Update)
{
  const float odds_hit = bbf::probabilityToOdds(0.8);
  const float odds_miss = bbf::probabilityToOdds(0.3);
  const char costmap0[3][3] =
      {
        { 10, 100, 100 },
        { 100, 100, 100 },
        { 100, 100, 100 },
      };
  const char costmap1[3][3] =
      {
        { 0, -1, 0 },
        { 100, 0, 0 },
        { 0, 0, 100 },
      };
  const float expected_odds[3][3] =
      {
        { bbf::MIN_ODDS, bbf::MIN_ODDS * odds_hit, bbf::MIN_ODDS * odds_hit * odds_miss },
        { bbf::MIN_ODDS * odds_hit * odds_hit, bbf::MIN_ODDS * odds_hit * odds_miss, bbf::MIN_ODDS },
        { bbf::MIN_ODDS * odds_hit * odds_miss, bbf::MIN_ODDS, bbf::MIN_ODDS },
      };
  const char expected_cost[3][3] =
      {
        { 0, 26, 8 },
        { 68, 8, 0 },
        { 8, 0, 0 },
      };
  BlockMemGridmap<char, 3, 2> cm;

  CostmapBBF bbf;
  bbf.reset(CostmapBBF::Vec(3, 3, 1));
  bbf.clear();

  cm.reset(CostmapBBF::Vec(3, 3, 1));
  for (int i = 0; i < 3; ++i)
    for (int j = 0; j < 3; ++j)
      cm[CostmapBBF::Vec(i, j, 0)] = costmap0[i][j];

  bbf.remember(&cm, CostmapBBF::Vec(0, 0, 0), odds_hit, odds_miss, 1, 2);

  for (int i = 0; i < 3; ++i)
    for (int j = 0; j < 3; ++j)
      cm[CostmapBBF::Vec(i, j, 0)] = costmap1[i][j];

  bbf.remember(&cm, CostmapBBF::Vec(0, 0, 0), odds_hit, odds_miss, 1, 2);

  const auto cb = [expected_odds](const CostmapBBF::Vec& p, bbf::BinaryBayesFilter& b)
  {
    EXPECT_FLOAT_EQ(expected_odds[p[0]][p[1]], b.get()) << "at " << p[0] << ", " << p[1];
  };
  bbf.forEach(cb);
  bbf.updateCostmap();
  for (int i = 0; i < 3; ++i)
    for (int j = 0; j < 3; ++j)
      EXPECT_EQ(expected_cost[i][j], bbf.getCost(CostmapBBF::Vec(i, j, 0))) << "at " << i << ", " << j;

  bbf.clear();
  const auto cb_cleared = [](const CostmapBBF::Vec& p, bbf::BinaryBayesFilter& b)
  {
    EXPECT_FLOAT_EQ(bbf::MIN_ODDS, b.get()) << "at " << p[0] << ", " << p[1];
  };
  bbf.forEach(cb_cleared);
  for (int i = 0; i < 3; ++i)
    for (int j = 0; j < 3; ++j)
      EXPECT_EQ(0, bbf.getCost(CostmapBBF::Vec(i, j, 0))) << "at " << i << ", " << j;
}
}  // namespace planner_3d
}  // namespace planner_cspace

int main(int argc, char** argv)
{
  testing::InitGoogleTest(&argc, argv);

  return RUN_ALL_TESTS();
}