47 assert(to && to->vertex());
51 Vector3d translation = delta;
52 double range2 = translation.squaredNorm();
55 translation.normalize();
57 double bearing = acos(translation.z());
58 if (fabs(bearing) >
_fov)
return false;
69 EdgeType::ErrorVector n =
_sampler.generateSample();
70 e->setMeasurement(e->measurement() + n);
80 std::list<PoseObject*>::reverse_iterator it = r->
trajectory().rbegin();
82 while (it != r->
trajectory().rend() && count < 1) {
89 for (std::set<BaseWorldObject*>::iterator it =
world()->objects().begin();
97 e->setMeasurementFromState();
OptimizableGraph * graph() const
const EstimateType & estimate() const
return the current estimate of the vertex
InformationType _information
WorldObjectType::VertexType VertexType
WorldObjectTrackXYZ WorldObjectType
EdgeType * mkEdge(WorldObjectType *object)
PoseObject * _robotPoseObject
const InformationType & information()
void setInformation(const InformationType &information_)
GaussianSampler< typename EdgeType::ErrorVector, InformationType > _sampler
BinarySensor(const std::string &name)
g2o edge from a track to a point node
TrajectoryType & trajectory()
bool isVisible(WorldObjectType *to)
RobotPoseType _sensorPose
virtual void addParameters()
void addNoise(EdgeType *e)
SensorPointXYZ(const std::string &name_)
ParameterSE3Offset * _offsetParam
std::set< BaseWorldObject * > & objects()
bool addParameter(Parameter *p)
WorldObject< VertexPointXYZ > WorldObjectTrackXYZ
Robot< WorldObjectSE3 > Robot3D
virtual bool addEdge(HyperGraph::Edge *e)