39 EdgeType::ErrorVector n =
_sampler.generateSample();
40 e->setMeasurement(e->measurement() + n);
47 assert(to && to->vertex());
52 Vector2d translation = delta;
53 double range2 = translation.squaredNorm();
56 translation.normalize();
57 double bearing = acos(translation.x());
58 if (fabs(bearing) >
_fov)
return false;
65 std::list<PoseObject*>::reverse_iterator it = r->
trajectory().rbegin();
67 while (it != r->
trajectory().rend() && count < 1) {
72 for (std::set<BaseWorldObject*>::iterator it =
world()->objects().begin();
78 e->setMeasurementFromState();
OptimizableGraph * graph() const
const EstimateType & estimate() const
return the current estimate of the vertex
WorldObjectType::VertexType VertexType
WorldObjectPointXY WorldObjectType
EdgeType * mkEdge(WorldObjectType *object)
PoseObject * _robotPoseObject
const InformationType & information()
GaussianSampler< typename EdgeType::ErrorVector, InformationType > _sampler
BinarySensor(const std::string &name)
TrajectoryType & trajectory()
virtual void addNoise(EdgeType *e)
bool isVisible(WorldObjectType *to)
EIGEN_MAKE_ALIGNED_OPERATOR_NEW SensorPointXY(const std::string &name_)
std::set< BaseWorldObject * > & objects()
WorldObject< VertexPointXY > WorldObjectPointXY
Robot< WorldObjectSE2 > Robot2D
virtual bool addEdge(HyperGraph::Edge *e)