| 40 | static constexpr unsigned int min_points_for_covariance_computation = 10; |
| 41 | |
| 42 | std::tuple<Eigen::Vector3d, Eigen::Vector3d> ComputeMeanAndNormal( |
| 43 | const map_closures::VoxelBlock &coordinates) { |
| 44 | const double num_points = static_cast<double>(coordinates.size()); |
| 45 | Eigen::Vector3d mean = |
| 46 | std::reduce(coordinates.cbegin(), coordinates.cend(), Eigen::Vector3d().setZero()) / |
| 47 | num_points; |
| 48 | |
| 49 | const Eigen::Matrix3d covariance = |
| 50 | std::transform_reduce(coordinates.cbegin(), coordinates.cend(), Eigen::Matrix3d().setZero(), |
| 51 | std::plus<Eigen::Matrix3d>(), |
| 52 | [&mean](const Eigen::Vector3d &point) { |
| 53 | Eigen::Vector3d centered = point - mean; |
| 54 | Eigen::Matrix3d S = centered * centered.transpose(); |
| 55 | return S; |
| 56 | }) / |
| 57 | (num_points - 1); |
| 58 | const Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> solver(covariance); |
| 59 | Eigen::Vector3d normal = solver.eigenvectors().col(0); |
| 60 | return {std::move(mean), std::move(normal)}; |
| 61 | } |
| 62 | |
| 63 | } // namespace |
| 64 |
no test coverage detected