40 template <
class MatrixType,
int DIM,
class Distance,
bool row_major>
41 struct KDTreeEigenMatrixAdaptor;
76 bool SetMatrixData(
const Eigen::MatrixXd &data);
80 bool SetGeometry(
const Geometry &geometry);
87 int Search(
const T &query,
89 std::vector<int> &indices,
90 std::vector<double> &distance2)
const;
93 int SearchKNN(
const T &query,
95 std::vector<int> &indices,
96 std::vector<double> &distance2)
const;
99 int SearchRadius(
const T &query,
101 std::vector<int> &indices,
102 std::vector<double> &distance2)
const;
104 template <
typename T>
105 int SearchHybrid(
const T &query,
108 std::vector<int> &indices,
109 std::vector<double> &distance2)
const;
116 bool SetRawData(
const Eigen::Map<const Eigen::MatrixXd> &data);
119 using KDTree_t = nanoflann::KDTreeEigenMatrixAdaptor<
120 Eigen::Map<const Eigen::MatrixXd>,
122 nanoflann::metric_L2,
128 size_t dimension_ = 0;
129 size_t dataset_size_ = 0;
std::unique_ptr< Eigen::Map< const Eigen::MatrixXd > > data_interface_
Definition: KDTreeFlann.h:126
The base geometry class.
Definition: Geometry.h:37
nanoflann::KDTreeEigenMatrixAdaptor< Eigen::Map< const Eigen::MatrixXd >, -1, nanoflann::metric_L2, false > KDTree_t
Definition: KDTreeFlann.h:123
Class to store featrues for registration.
Definition: Feature.h:47
Base class for KDTree search parameters.
Definition: KDTreeSearchParam.h:35
KDTree with FLANN for nearest neighbor search.
Definition: KDTreeFlann.h:51
Definition: PinholeCameraIntrinsic.cpp:35
std::vector< double > data_
Definition: KDTreeFlann.h:125
std::unique_ptr< KDTree_t > nanoflann_index_
Definition: KDTreeFlann.h:127
const char const char value recording_handle imu_sample recording_handle uint8_t data
Definition: K4aPlugin.cpp:274