This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 123]
  Assignments | |
| Cluster | Hypotheses cluster class |
 ColorRGBA | |
  Color | Color class, extension to std_msgs::ColorRGBA |
| tree< T, tree_node_allocator >::compare_nodes< StrictWeakOrdering > | Comparator class for two nodes of a tree (used for sorting and searching) |
| Conversion | |
| EKFilter | |
| nonholonomicEKFilter | Nonholonomic Kalman filter |
| EKFilter | |
| constant_velocity_ekfilter | Constant velocity Kalman filter |
| constantVelocityEKFilter | Constant velocity Kalman filter |
 MotionModel | Nonholonomic Motion model abstraction layer |
 Target | |
| GNN | |
| GVGraph | An object containing a libgraph graph and its associated nodes and edges. |
| Hypothesis | Hypotheses tree nodes |
| tree< T, tree_node_allocator >::iterator_base | Base class for iterators, only pointers stored, no traversal logic |
| tree< T, tree_node_allocator >::breadth_first_queued_iterator | Breadth-first iterator, using a queue |
| tree< T, tree_node_allocator >::breadth_first_queued_iterator | Breadth-first iterator, using a queue |
| tree< T, tree_node_allocator >::children_iterator | Iterator which traverses only the nodes which are siblings of each other |
| tree< T, tree_node_allocator >::fixed_depth_iterator | Iterator which traverses only the nodes at a given depth from the root |
| tree< T, tree_node_allocator >::leaf_iterator | Iterator which traverses only the leaves |
| tree< T, tree_node_allocator >::leaf_iterator | Iterator which traverses only the leaves |
| tree< T, tree_node_allocator >::post_order_iterator | Depth-first iterator, first accessing the children, then the node itself |
| tree< T, tree_node_allocator >::post_order_iterator | Depth-first iterator, first accessing the children, then the node itself |
| tree< T, tree_node_allocator >::pre_order_iterator | Depth-first iterator, first accessing the node, then its children |
| tree< T, tree_node_allocator >::pre_order_iterator | Depth-first iterator, first accessing the node, then its children |
| tree< T, tree_node_allocator >::sibling_iterator | Iterator which traverses only the nodes which are siblings of each other |
| tree< T, tree_node_allocator >::iterator_base_less | Comparator class for iterators (compares pointer values; why doesn't this work automatically?) |
| Markers | Dynamic markers support class |
| Matrix< T > | |
| Matrix< double > | |
| Matrix< int > | |
| Measurement | Measurement class type |
| Mht | Big and complicated class that implements the MHT (Multiple Hypotheses Tracking) algorithm |
| Munkres | |
| Node | |
| orderedPair | |
| PointCloudGeneretor | |
| PointXYZ | |
| Point | Simple point class |
| SearchArea | Ellipsoid target gait area |
| st_cluster | This structure contains all point cluster information |
| st_line | This structure contains a single line properties |
| st_object | This structure contains object information |
| t_classification | Object classification in regard to velocity and occlusion |
| t_cluster | Cluster type class, clusters are groups of points in close proximity |
| t_config | This structure contains global configurations parameters |
| t_data | This structure has all points coordinates |
| t_errors | Estimation errors |
| t_flag | This structure contains global flags parameters |
| t_fps | Iterations per second structure |
| t_linked_list | Full description of and tracked object |
| t_measurements | Single XY measurement |
| t_motion_models | Motion models structure |
| t_node | Class handler for tree nodes |
| t_object_morphology | Morphologic information of target |
| t_path | List of XY points |
| t_point | Polar point structure |
| t_position | Position structure, estimated and predicted values |
| t_search_area | Ellipsoid target gait area |
| t_timers | Timers structure |
| t_velocity | Target velocity data |
| Timer | |
| tree< T, tree_node_allocator > | |
| tree< HypothesisPtr > | |
| HypothesisTree | Hypotheses tree |
| tree_node_< T > | A node in the tree, combining links to other nodes as well as the actual data |