48 assert(to && to->vertex());
50 VertexType::EstimateType pose = v->estimate();
51 VertexType::EstimateType delta =
_sensorPose.inverse() * pose;
52 Vector2d translation = delta;
53 double range2 = translation.squaredNorm();
56 translation.normalize();
58 double bearing = atan2(translation.y(), translation.x());
59 if (fabs(bearing) >
_fov)
return false;
72 e->setMeasurement(e->measurement() + n);
82 std::list<PoseObject*>::reverse_iterator it = r->trajectory().rbegin();
84 while (it != r->trajectory().rend() && count < 1) {
91 for (std::set<BaseWorldObject*>::iterator it =
world()->
objects().begin();
99 e->setMeasurementFromState();
internal::BaseEdgeTraits< D >::ErrorVector ErrorVector
OptimizableGraph * graph() const
InformationType _information
WorldObjectType::VertexType VertexType
WorldObjectPointXY WorldObjectType
EdgeType * mkEdge(WorldObjectType *object)
PoseObject * _robotPoseObject
const InformationType & information()
void setInformation(const InformationType &information_)
GaussianSampler< typename EdgeType::ErrorVector, InformationType > _sampler
EdgeSE2PointXYOffset EdgeType
g2o edge from a track to a point node
SampleType generateSample()
return a sample of the Gaussian distribution
const SE2 & offset() const
SensorPointXYOffset(const std::string &name_)
bool isVisible(WorldObjectType *to)
virtual void addParameters()
ParameterSE2Offset * _offsetParam
virtual void addNoise(EdgeType *e)
RobotPoseType _sensorPose
std::set< BaseWorldObject * > & objects()
bool addParameter(Parameter *p)
WorldObject< VertexPointXY > WorldObjectPointXY
Robot< WorldObjectSE2 > Robot2D
virtual bool addEdge(HyperGraph::Edge *e)