29 using namespace Eigen;
45 assert(to && to->
vertex());
47 VertexType::EstimateType pose=v->estimate();
48 VertexType::EstimateType delta =
_robotPoseObject->vertex()->estimate().inverse()*pose;
49 Vector2d translation=delta;
50 double range2=translation.squaredNorm();
55 translation.normalize();
56 double bearing=acos(translation.x());
57 if (fabs(bearing)>
_fov)
66 std::list<PoseObject*>::reverse_iterator it=r->
trajectory().rbegin();
68 while (it!=r->
trajectory().rend() && count < 1){
74 for (std::set<BaseWorldObject*>::iterator it=
world()->
objects().begin();
WorldObjectType::VertexType VertexType
virtual void addNoise(EdgeType *e)
virtual bool setMeasurementFromState()
GaussianSampler< typename EdgeType::ErrorVector, InformationType > _sampler
BaseEdge< D, Vector2D >::ErrorVector ErrorVector
SampleType generateSample()
virtual void setMeasurement(const Measurement &m)
const InformationType & information()
EIGEN_STRONG_INLINE void setInformation(const InformationType &information)
TrajectoryType & trajectory()
PoseObject * _robotPoseObject
OptimizableGraph * graph()
bool isVisible(WorldObjectType *to)
std::set< BaseWorldObject * > & objects()
virtual bool addEdge(HyperGraph::Edge *e)
EdgeType * mkEdge(WorldObjectType *object)
EIGEN_STRONG_INLINE const Measurement & measurement() const
accessor functions for the measurement represented by the edge