C++ UTimer类代码示例

void DataRecorder::addData(const rtabmap::SensorData & data, const Transform & pose, const cv::Mat & covariance)
{
	memoryMutex_.lock();
	if(memory_)
	{
		if(memory_->getStMem().size() == 0 && data.id() > 0)
		{
			ParametersMap customParameters;
			customParameters.insert(ParametersPair(Parameters::kMemGenerateIds(), "false")); // use id from data
			memory_->parseParameters(customParameters);
		}

		//save to database
		UTimer time;
		memory_->update(data, pose, covariance);
		const Signature * s = memory_->getLastWorkingSignature();
		totalSizeKB_ += (int)s->sensorData().imageCompressed().total()/1000;
		totalSizeKB_ += (int)s->sensorData().depthOrRightCompressed().total()/1000;
		totalSizeKB_ += (int)s->sensorData().laserScanCompressed().total()/1000;
		memory_->cleanup();

		if(++count_ % 30)
		{
			memory_->emptyTrash();
		}
		UDEBUG("Time to process a message = %f s", time.ticks());
	}
	memoryMutex_.unlock();
}

Transform Odometry::process(const SensorData & data, OdometryInfo * info)
{
	if(_pose.isNull())
	{
		_pose.setIdentity(); // initialized
	}

	UASSERT(!data.image().empty());
	if(dynamic_cast<OdometryMono*>(this) == 0)
	{
		UASSERT(!data.depthOrRightImage().empty());
	}

	if(data.fx() <= 0 || data.fyOrBaseline() <= 0)
	{
		UERROR("Rectified images required! Calibrate your camera. (fx=%f, fy/baseline=%f, cx=%f, cy=%f)",
				data.fx(), data.fyOrBaseline(), data.cx(), data.cy());
		return Transform();
	}

	UTimer time;
	Transform t = this->computeTransform(data, info);

	if(info)
	{
		info->time = time.elapsed();
		info->lost = t.isNull();
	}

	if(!t.isNull())
	{
		_resetCurrentCount = _resetCountdown;

		if(_force2D)
		{
			float x,y,z, roll,pitch,yaw;
			t.getTranslationAndEulerAngles(x, y, z, roll, pitch, yaw);
			t = Transform(x,y,0, 0,0,yaw);
		}

		return _pose *= t; // updated
	}
	else if(_resetCurrentCount > 0)
	{
		UWARN("Odometry lost! Odometry will be reset after next %d consecutive unsuccessful odometry updates...", _resetCurrentCount);

		--_resetCurrentCount;
		if(_resetCurrentCount == 0)
		{
			UWARN("Odometry automatically reset to latest pose!");
			this->reset(_pose);
		}
	}

	return Transform();
}

SensorData Camera::takeImage(CameraInfo * info)
{
	bool warnFrameRateTooHigh = false;
	float actualFrameRate = 0;
	if(_imageRate>0)
	{
		int sleepTime = (1000.0f/_imageRate - 1000.0f*_frameRateTimer->getElapsedTime());
		if(sleepTime > 2)
		{
			uSleep(sleepTime-2);
		}
		else if(sleepTime < 0)
		{
			warnFrameRateTooHigh = true;
			actualFrameRate = 1.0/(_frameRateTimer->getElapsedTime());
		}

		// Add precision at the cost of a small overhead
		while(_frameRateTimer->getElapsedTime() < 1.0/double(_imageRate)-0.000001)
		{
			//
		}

		double slept = _frameRateTimer->getElapsedTime();
		_frameRateTimer->start();
		UDEBUG("slept=%fs vs target=%fs", slept, 1.0/double(_imageRate));
	}

	UTimer timer;
	SensorData data  = this->captureImage(info);
	double captureTime = timer.ticks();
	if(warnFrameRateTooHigh)
	{
		UWARN("Camera: Cannot reach target image rate %f Hz, current rate is %f Hz and capture time = %f s.",
				_imageRate, actualFrameRate, captureTime);
	}
	else
	{
		UDEBUG("Time capturing image = %fs", captureTime);
	}
	if(info)
	{
		info->id = data.id();
		info->stamp = data.stamp();
		info->timeCapture = captureTime;
	}
	return data;
}

	void mapDataReceivedCallback(const rtabmap_ros::MapDataConstPtr & msg)
	{
		UTimer timer;

		std::map<int, Transform> poses;
		std::multimap<int, Link> constraints;
		Transform mapOdom;
		rtabmap_ros::mapGraphFromROS(msg->graph, poses, constraints, mapOdom);
		for(unsigned int i=0; i<msg->nodes.size(); ++i)
		{
			if(msg->nodes[i].image.size() ||
			   msg->nodes[i].depth.size() ||
			   msg->nodes[i].laserScan.size())
			{
				uInsert(nodes_, std::make_pair(msg->nodes[i].id, rtabmap_ros::nodeDataFromROS(msg->nodes[i])));
			}
		}

		// create a tmp signature with latest sensory data
		if(poses.size() && nodes_.find(poses.rbegin()->first) != nodes_.end())
		{
			Signature tmpS = nodes_.at(poses.rbegin()->first);
			SensorData tmpData = tmpS.sensorData();
			tmpData.setId(-1);
			uInsert(nodes_, std::make_pair(-1, Signature(-1, -1, 0, tmpS.getStamp(), "", tmpS.getPose(), Transform(), tmpData)));
			poses.insert(std::make_pair(-1, poses.rbegin()->second));
		}

		// Update maps
		poses = mapsManager_.updateMapCaches(
				poses,
				0,
				false,
				false,
				false,
				false,
				false,
				nodes_);

		mapsManager_.publishMaps(poses, msg->header.stamp, msg->header.frame_id);

		ROS_INFO("map_assembler: Publishing data = %fs", timer.ticks());
	}

// returned OcTree must be deleted
// RTAB-Map optimizes the graph at almost each iteration, an octomap cannot
// be updated online. Only available on service. To have an "online" octomap published as a topic,
// you may want to subscribe an octomap_server to /rtabmap/cloud topic.
//
octomap::OcTree * MapsManager::createOctomap(const std::map<int, Transform> & poses)
{
	octomap::OcTree * octree = new octomap::OcTree(gridCellSize_);
	UTimer time;
	for(std::map<int, Transform>::const_iterator posesIter = poses.begin(); posesIter!=poses.end(); ++posesIter)
	{
		std::map<int, pcl::PointCloud<pcl::PointXYZRGB>::Ptr >::iterator cloudsIter = clouds_.find(posesIter->first);
		if(cloudsIter != clouds_.end() && cloudsIter->second->size())
		{
			octomap::Pointcloud * scan = new octomap::Pointcloud();

			//octomap::pointcloudPCLToOctomap(*cloudsIter->second, *scan); // Not anymore in Indigo!
			scan->reserve(cloudsIter->second->size());
			for(pcl::PointCloud<pcl::PointXYZRGB>::const_iterator it = cloudsIter->second->begin();
				it != cloudsIter->second->end();
				++it)
			{
				// Check if the point is invalid
				if(pcl::isFinite(*it))
				{
					scan->push_back(it->x, it->y, it->z);
				}
			}

			float x,y,z, r,p,w;
			posesIter->second.getTranslationAndEulerAngles(x,y,z,r,p,w);
			octomap::ScanNode node(scan, octomap::pose6d(x,y,z, r,p,w), posesIter->first);
			octree->insertPointCloud(node, cloudMaxDepth_, true, true);
			ROS_INFO("inserted %d pt=%d (%fs)", posesIter->first, (int)scan->size(), time.ticks());
		}
	}

	octree->updateInnerOccupancy();
	ROS_INFO("updated inner occupancy (%fs)", time.ticks());

	// clear memory if no one subscribed
	if(mapCacheCleanup_ && cloudMapPub_.getNumSubscribers() == 0)
	{
		clouds_.clear();
	}
	return octree;
}

void CameraThread::mainLoop()
{
	UTimer totalTime;
	UDEBUG("");
	CameraInfo info;
	SensorData data = _camera->takeImage(&info);

	if(!data.imageRaw().empty() || (dynamic_cast<DBReader*>(_camera) != 0 && data.id()>0)) // intermediate nodes could not have image set
	{
		postUpdate(&data, &info);

		info.cameraName = _camera->getSerial();
		info.timeTotal = totalTime.ticks();
		this->post(new CameraEvent(data, info));
	}
	else if(!this->isKilled())
	{
		UWARN("no more images...");
		this->kill();
		this->post(new CameraEvent());
	}
}

//.........这里部分代码省略.........
	rtabmap::DBReader reader(databasePath, -rate, false, false, false, startId);
	if(!reader.init())
	{
		ROS_ERROR("Cannot open database \"%s\".", databasePath.c_str());
		return -1;
	}

	ros::ServiceServer pauseSrv = pnh.advertiseService("pause", pauseCallback);
	ros::ServiceServer resumeSrv = pnh.advertiseService("resume", resumeCallback);

	image_transport::ImageTransport it(nh);
	image_transport::Publisher imagePub;
	image_transport::Publisher rgbPub;
	image_transport::Publisher depthPub;
	image_transport::Publisher leftPub;
	image_transport::Publisher rightPub;
	ros::Publisher rgbCamInfoPub;
	ros::Publisher depthCamInfoPub;
	ros::Publisher leftCamInfoPub;
	ros::Publisher rightCamInfoPub;
	ros::Publisher odometryPub;
	ros::Publisher scanPub;
	ros::Publisher scanCloudPub;
	ros::Publisher globalPosePub;
	ros::Publisher gpsFixPub;
	ros::Publisher clockPub;
	tf2_ros::TransformBroadcaster tfBroadcaster;

	if(publishClock)
	{
		clockPub = nh.advertise<rosgraph_msgs::Clock>("/clock", 1);
	}

	UTimer timer;
	rtabmap::CameraInfo cameraInfo;
	rtabmap::SensorData data = reader.takeImage(&cameraInfo);
	rtabmap::OdometryInfo odomInfo;
	odomInfo.reg.covariance = cameraInfo.odomCovariance;
	rtabmap::OdometryEvent odom(data, cameraInfo.odomPose, odomInfo);
	double acquisitionTime = timer.ticks();
	while(ros::ok() && odom.data().id())
	{
		ROS_INFO("Reading sensor data %d...", odom.data().id());

		ros::Time time(odom.data().stamp());

		if(publishClock)
		{
			rosgraph_msgs::Clock msg;
			msg.clock = time;
			clockPub.publish(msg);
		}

		sensor_msgs::CameraInfo camInfoA; //rgb or left
		sensor_msgs::CameraInfo camInfoB; //depth or right

		camInfoA.K.assign(0);
		camInfoA.K[0] = camInfoA.K[4] = camInfoA.K[8] = 1;
		camInfoA.R.assign(0);
		camInfoA.R[0] = camInfoA.R[4] = camInfoA.R[8] = 1;
		camInfoA.P.assign(0);
		camInfoA.P[10] = 1;

		camInfoA.header.frame_id = cameraFrameId;
		camInfoA.header.stamp = time;

//.........这里部分代码省略.........
			for(std::map<int, rtabmap::Transform>::iterator iter=poses.begin(); iter!=poses.end(); ++iter)
			{
				int id = iter->first;
				if(!iter->second.isNull())
				{
					if(main_scene_.hasCloud(id))
					{
						//just update pose
						main_scene_.setCloudPose(id, iter->second);
						main_scene_.setCloudVisible(id, true);
						std::map<int, Mesh>::iterator meshIter = createdMeshes_.find(id);
						if(meshIter!=createdMeshes_.end())
						{
							meshIter->second.pose = iter->second;
						}
						else
						{
							UERROR("Not found mesh %d !?!?", id);
						}
					}
					else if(uContains(bufferedSensorData, id))
					{
						rtabmap::SensorData & data = bufferedSensorData.at(id);
						if(!data.imageRaw().empty() && !data.depthRaw().empty())
						{
							// Voxelize and filter depending on the previous cloud?
							pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud;
							pcl::IndicesPtr indices(new std::vector<int>);
							LOGI("Creating node cloud %d (image size=%dx%d)", id, data.imageRaw().cols, data.imageRaw().rows);
							cloud = rtabmap::util3d::cloudRGBFromSensorData(data, data.imageRaw().rows/data.depthRaw().rows, maxCloudDepth_, 0, indices.get());

							if(cloud->size() && indices->size())
							{
								UTimer time;

								// pcl::organizedFastMesh doesn't take indices, so set to NaN points we don't need to mesh
								pcl::PointCloud<pcl::PointXYZRGB>::Ptr output(new pcl::PointCloud<pcl::PointXYZRGB>);
								pcl::ExtractIndices<pcl::PointXYZRGB> filter;
								filter.setIndices(indices);
								filter.setKeepOrganized(true);
								filter.setInputCloud(cloud);
								filter.filter(*output);

								std::vector<pcl::Vertices> polygons = rtabmap::util3d::organizedFastMesh(output, meshAngleToleranceDeg_*M_PI/180.0, false, meshTrianglePix_);
								pcl::PointCloud<pcl::PointXYZRGB>::Ptr outputCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
								std::vector<pcl::Vertices> outputPolygons;

								outputCloud = output;
								outputPolygons = polygons;

								LOGI("Creating mesh, %d polygons (%fs)", (int)outputPolygons.size(), time.ticks());

								if(outputCloud->size() && outputPolygons.size())
								{
									totalPolygons_ += outputPolygons.size();

									main_scene_.addCloud(id, outputCloud, outputPolygons, iter->second, data.imageRaw());


									// protect createdMeshes_ used also by exportMesh() method
									std::pair<std::map<int, Mesh>::iterator, bool> inserted = createdMeshes_.insert(std::make_pair(id, Mesh()));
									UASSERT(inserted.second);
									inserted.first->second.cloud = outputCloud;
									inserted.first->second.polygons = outputPolygons;
									inserted.first->second.pose = iter->second;
									inserted.first->second.texture = data.imageCompressed();

// return not null transform if odometry is correctly computed
Transform OdometryICP::computeTransform(const SensorData & data, OdometryInfo * info)
{
	UTimer timer;
	Transform output;

	bool hasConverged = false;
	double variance = 0;
	unsigned int minPoints = 100;
	if(!data.depth().empty())
	{
		if(data.depth().type() == CV_8UC1)
		{
			UERROR("ICP 3D cannot be done on stereo images!");
			return output;
		}

		pcl::PointCloud<pcl::PointXYZ>::Ptr newCloudXYZ = util3d::getICPReadyCloud(
						data.depth(),
						data.fx(),
						data.fy(),
						data.cx(),
						data.cy(),
						_decimation,
						this->getMaxDepth(),
						_voxelSize,
						_samples,
						data.localTransform());

		if(_pointToPlane)
		{
			pcl::PointCloud<pcl::PointNormal>::Ptr newCloud = util3d::computeNormals(newCloudXYZ);

			std::vector<int> indices;
			newCloud = util3d::removeNaNNormalsFromPointCloud<pcl::PointNormal>(newCloud);
			if(newCloudXYZ->size() != newCloud->size())
			{
				UWARN("removed nan normals...");
			}

			if(_previousCloudNormal->size() > minPoints && newCloud->size() > minPoints)
			{
				int correspondences = 0;
				Transform transform = util3d::icpPointToPlane(newCloud,
						_previousCloudNormal,
						_maxCorrespondenceDistance,
						_maxIterations,
						&hasConverged,
						&variance,
						&correspondences);

				// verify if there are enough correspondences
				float correspondencesRatio = float(correspondences)/float(_previousCloudNormal->size()>newCloud->size()?_previousCloudNormal->size():newCloud->size());

				if(!transform.isNull() && hasConverged &&
		   	   	   correspondencesRatio >= _correspondenceRatio)
				{
					output = transform;
					_previousCloudNormal = newCloud;
				}
				else
				{
					UWARN("Transform not valid (hasConverged=%s variance = %f)",
							hasConverged?"true":"false", variance);
				}
			}
			else if(newCloud->size() > minPoints)
			{
				output.setIdentity();
				_previousCloudNormal = newCloud;
			}
		}
		else
		{
			//point to point
			if(_previousCloud->size() > minPoints && newCloudXYZ->size() > minPoints)
			{
				int correspondences = 0;
				Transform transform = util3d::icp(newCloudXYZ,
						_previousCloud,
						_maxCorrespondenceDistance,
						_maxIterations,
						&hasConverged,
						&variance,
						&correspondences);

				// verify if there are enough correspondences
				float correspondencesRatio = float(correspondences)/float(_previousCloud->size()>newCloudXYZ->size()?_previousCloud->size():newCloudXYZ->size());

				if(!transform.isNull() && hasConverged &&
				   correspondencesRatio >= _correspondenceRatio)
				{
					output = transform;
					_previousCloud = newCloudXYZ;
				}
				else
				{
					UWARN("Transform not valid (hasConverged=%s variance = %f)",
							hasConverged?"true":"false", variance);
				}
//.........这里部分代码省略.........

void UAppli::addTimeout(unsigned long _delay, int _ntimes, UCall& c) {
  UTimer* t = new UTimer(_delay, _ntimes, true); // true => auto_delete
  t->onTimeout(c);
  t->start();
}

cv::Mat Camera::takeImage(cv::Mat & descriptors, std::vector<cv::KeyPoint> & keypoints)
{
	descriptors = cv::Mat();
	keypoints.clear();
	float imageRate = _imageRate==0.0f?33.0f:_imageRate; // limit to 33Hz if infinity
	if(imageRate>0)
	{
		int sleepTime = (1000.0f/imageRate - 1000.0f*_frameRateTimer->getElapsedTime());
		if(sleepTime > 2)
		{
			uSleep(sleepTime-2);
		}

		// Add precision at the cost of a small overhead
		while(_frameRateTimer->getElapsedTime() < 1.0/double(imageRate)-0.000001)
		{
			//
		}

		double slept = _frameRateTimer->getElapsedTime();
		_frameRateTimer->start();
		UDEBUG("slept=%fs vs target=%fs", slept, 1.0/double(imageRate));
	}

	cv::Mat img;
	UTimer timer;
	img = this->captureImage();
	UDEBUG("Time capturing image = %fs", timer.ticks());

	if(!img.empty())
	{
		if(img.depth() != CV_8U)
		{
			UWARN("Images should have already 8U depth !?");
			cv::Mat tmp = img;
			img = cv::Mat();
			tmp.convertTo(img, CV_8U);
			UDEBUG("Time converting image to 8U = %fs", timer.ticks());
		}

		if(_featuresExtracted && _keypointDetector && _keypointDescriptor)
		{
			keypoints = _keypointDetector->generateKeypoints(img);
			descriptors = _keypointDescriptor->generateDescriptors(img, keypoints);
			UDEBUG("Post treatment time = %fs", timer.ticks());
		}

		if(_framesDropped)
		{
			unsigned int count = 0;
			while(count++ < _framesDropped)
			{
				cv::Mat tmp = this->captureImage();
				if(!tmp.empty())
				{
					UDEBUG("frame dropped (%d/%d)", (int)count, (int)_framesDropped);
				}
				else
				{
					break;
				}
			}
			UDEBUG("Frames dropped time = %fs", timer.ticks());
		}
	}
	return img;
}

cv::Mat BayesFilter::generatePrediction(const Memory * memory, const std::vector<int> & ids) const
{
	if(!_fullPredictionUpdate && !_prediction.empty())
	{
		return updatePrediction(_prediction, memory, uKeys(_posterior), ids);
	}
	UDEBUG("");

	UASSERT(memory &&
		   _predictionLC.size() >= 2 &&
		   ids.size());

	UTimer timer;
	timer.start();
	UTimer timerGlobal;
	timerGlobal.start();

	std::map<int, int> idToIndexMap;
	for(unsigned int i=0; i<ids.size(); ++i)
	{
		UASSERT_MSG(ids[i] != 0, "Signature id is null ?!?");
		idToIndexMap.insert(idToIndexMap.end(), std::make_pair(ids[i], i));
	}

	//int rows = prediction.rows;
	cv::Mat prediction = cv::Mat::zeros(ids.size(), ids.size(), CV_32FC1);
	int cols = prediction.cols;

	// Each prior is a column vector
	UDEBUG("_predictionLC.size()=%d",_predictionLC.size());
	std::set<int> idsDone;

	for(unsigned int i=0; i<ids.size(); ++i)
	{
		if(idsDone.find(ids[i]) == idsDone.end())
		{
			if(ids[i] > 0)
			{
				// Set high values (gaussians curves) to loop closure neighbors

				// ADD prob for each neighbors
				std::map<int, int> neighbors = memory->getNeighborsId(ids[i], _predictionLC.size()-1, 0);
				std::list<int> idsLoopMargin;
				//filter neighbors in STM
				for(std::map<int, int>::iterator iter=neighbors.begin(); iter!=neighbors.end();)
				{
					if(memory->isInSTM(iter->first))
					{
						neighbors.erase(iter++);
					}
					else
					{
						if(iter->second == 0)
						{
							idsLoopMargin.push_back(iter->second);
						}
						++iter;
					}
				}

				// should at least have 1 id in idsMarginLoop
				if(idsLoopMargin.size() == 0)
				{
					UFATAL("No 0 margin neighbor for signature %d !?!?", ids[i]);
				}

				// same neighbor tree for loop signatures (margin = 0)
				for(std::list<int>::iterator iter = idsLoopMargin.begin(); iter!=idsLoopMargin.end(); ++iter)
				{
					float sum = 0.0f; // sum values added
					sum += this->addNeighborProb(prediction, i, neighbors, idToIndexMap);
					idsDone.insert(*iter);
					this->normalize(prediction, i, sum, ids[0]<0);
				}
			}
			else
			{
				// Set the virtual place prior
				if(_virtualPlacePrior > 0)
				{
					if(cols>1) // The first must be the virtual place
					{
						((float*)prediction.data)[i] = _virtualPlacePrior;
						float val = (1.0-_virtualPlacePrior)/(cols-1);
						for(int j=1; j<cols; j++)
						{
							((float*)prediction.data)[i + j*cols] = val;
						}
					}
					else if(cols>0)
					{
						((float*)prediction.data)[i] = 1;
					}
				}
				else
				{
					// Only for some tests...
					// when _virtualPlacePrior=0, set all priors to the same value
					if(cols>1)
					{
//.........这里部分代码省略.........

//.........这里部分代码省略.........
				else
				{
					value = uReplaceChar(value, ',', ' ');
				}
				std::pair<ParametersMap::iterator, bool> inserted = pm.insert(ParametersPair(key, value));
				if(inserted.second == false)
				{
					inserted.first->second = value;
				}
			}
			else
			{
				showUsage();
			}
			continue;
		}

		printf("Unrecognized option : %s\n", argv[i]);
		showUsage();
	}

	if(repeat && createGT)
	{
		printf("Cannot create a Ground truth if repeat is on.\n");
		showUsage();
	}
	else if((imageWidth && imageHeight == 0) ||
			(imageHeight && imageWidth == 0))
	{
		printf("If imageWidth is set, imageHeight must be too.\n");
		showUsage();
	}

	UTimer timer;
	timer.start();
	std::queue<double> iterationMeanTime;

	Camera * camera = 0;
	if(UDirectory::exists(path))
	{
		camera = new CameraImages(path, startAt, false, 1/rate, imageWidth, imageHeight);
	}
	else
	{
		camera = new CameraVideo(path, 1/rate, imageWidth, imageHeight);
	}

	if(!camera || !camera->init())
	{
		printf("Camera init failed, using path \"%s\"\n", path.c_str());
		exit(1);
	}

	std::map<int, int> groundTruth;

	ULogger::setType(ULogger::kTypeConsole);
	//ULogger::setType(ULogger::kTypeFile, rtabmap.getWorkingDir()+"/LogConsole.txt", false);
	//ULogger::setBuffered(true);
	ULogger::setLevel(logLevel);
	ULogger::setExitLevel(exitLevel);

	// Create tasks : memory is deleted
	Rtabmap rtabmap;
	// Disable statistics (we don't need them)
	pm.insert(ParametersPair(Parameters::kRtabmapPublishStats(), "false"));
	// use default working directory

	void mapDataReceivedCallback(const rtabmap_ros::MapDataConstPtr & msg)
	{
		// save new poses and constraints
		// Assuming that nodes/constraints are all linked together
		UASSERT(msg->graph.posesId.size() == msg->graph.poses.size());

		bool dataChanged = false;

		std::multimap<int, Link> newConstraints;
		for(unsigned int i=0; i<msg->graph.links.size(); ++i)
		{
			Link link = rtabmap_ros::linkFromROS(msg->graph.links[i]);
			newConstraints.insert(std::make_pair(link.from(), link));

			bool edgeAlreadyAdded = false;
			for(std::multimap<int, Link>::iterator iter = cachedConstraints_.lower_bound(link.from());
					iter != cachedConstraints_.end() && iter->first == link.from();
					++iter)
			{
				if(iter->second.to() == link.to())
				{
					edgeAlreadyAdded = true;
					if(iter->second.transform().getDistanceSquared(link.transform()) > 0.0001)
					{
						ROS_WARN("%d ->%d (%s vs %s)",iter->second.from(), iter->second.to(), iter->second.transform().prettyPrint().c_str(),
								link.transform().prettyPrint().c_str());
						dataChanged = true;
					}
				}
			}
			if(!edgeAlreadyAdded)
			{
				cachedConstraints_.insert(std::make_pair(link.from(), link));
			}
		}

		std::map<int, Signature> newNodeInfos;
		// add new odometry poses
		for(unsigned int i=0; i<msg->nodes.size(); ++i)
		{
			int id = msg->nodes[i].id;
			Transform pose = rtabmap_ros::transformFromPoseMsg(msg->nodes[i].pose);
			Signature s = rtabmap_ros::nodeInfoFromROS(msg->nodes[i]);
			newNodeInfos.insert(std::make_pair(id, s));

			std::pair<std::map<int, Signature>::iterator, bool> p = cachedNodeInfos_.insert(std::make_pair(id, s));
			if(!p.second && pose.getDistanceSquared(cachedNodeInfos_.at(id).getPose()) > 0.0001)
			{
				dataChanged = true;
			}
		}

		if(dataChanged)
		{
			ROS_WARN("Graph data has changed! Reset cache...");
			cachedConstraints_ = newConstraints;
			cachedNodeInfos_ = newNodeInfos;
		}

		//match poses in the graph
		std::multimap<int, Link> constraints;
		std::map<int, Signature> nodeInfos;
		if(globalOptimization_)
		{
			constraints = cachedConstraints_;
			nodeInfos = cachedNodeInfos_;
		}
		else
		{
			constraints = newConstraints;
			for(unsigned int i=0; i<msg->graph.posesId.size(); ++i)
			{
				std::map<int, Signature>::iterator iter = cachedNodeInfos_.find(msg->graph.posesId[i]);
				if(iter != cachedNodeInfos_.end())
				{
					nodeInfos.insert(*iter);
				}
				else
				{
					ROS_ERROR("Odometry pose of node %d not found in cache!", msg->graph.posesId[i]);
					return;
				}
			}
		}

		std::map<int, Transform> poses;
		for(std::map<int, Signature>::iterator iter=nodeInfos.begin(); iter!=nodeInfos.end(); ++iter)
		{
			poses.insert(std::make_pair(iter->first, iter->second.getPose()));
		}

		// Optimize only if there is a subscriber
		if(mapDataPub_.getNumSubscribers() || mapGraphPub_.getNumSubscribers())
		{
			UTimer timer;
			std::map<int, Transform> optimizedPoses;
			Transform mapCorrection = Transform::getIdentity();
			std::map<int, rtabmap::Transform> posesOut;
			std::multimap<int, rtabmap::Link> linksOut;
			if(poses.size() > 1 && constraints.size() > 0)
//.........这里部分代码省略.........

int main(int argc, char * argv[])
{
	if(argc < 2)
	{
		showUsage();
	}
	ULogger::setType(ULogger::kTypeConsole);
	ULogger::setLevel(ULogger::kDebug);

	std::string dictionaryPath = argv[argc-1];
	std::list<std::vector<float> > objectDescriptors;
	//std::list<std::vector<float> > descriptors;
	std::map<int, std::vector<float> > descriptors;
	int dimension  = 0;
	UTimer timer;
	int objectDescriptorsSize= 400;

	std::ifstream file;
	if(!dictionaryPath.empty())
	{
		file.open(dictionaryPath.c_str(), std::ifstream::in);
	}
	if(file.good())
	{
		UDEBUG("Loading the dictionary from \"%s\"", dictionaryPath.c_str());

		// first line is the header
		std::string str;
		std::list<std::string> strList;
		std::getline(file, str);
		strList = uSplitNumChar(str);
		for(std::list<std::string>::iterator iter = strList.begin(); iter != strList.end(); ++iter)
		{
			if(uIsDigit(iter->at(0)))
			{
				dimension = std::atoi(iter->c_str());
				break;
			}
		}

		if(dimension == 0 || dimension > 1000)
		{
			UERROR("Invalid dictionary file, visual word dimension (%d) is not valid, \"%s\"", dimension, dictionaryPath.c_str());
		}
		else
		{
			int descriptorsLoaded = 0;
			// Process all words
			while(file.good())
			{
				std::getline(file, str);
				strList = uSplit(str);
				if((int)strList.size() == dimension+1)
				{
					//first one is the visual word id
					std::list<std::string>::iterator iter = strList.begin();
					int id = atoi(iter->c_str());
					++iter;

					std::vector<float> descriptor(dimension);
					int i=0;

					//get descriptor
					for(;i<dimension && iter != strList.end(); ++i, ++iter)
					{
						descriptor[i] = uStr2Float(*iter);
					}
					if(i != dimension)
					{
						UERROR("");
					}

					if(++descriptorsLoaded<=objectDescriptorsSize)
					{
						objectDescriptors.push_back(descriptor);
					}
					else
					{
						//descriptors.push_back(descriptor);
						descriptors.insert(std::make_pair(id, descriptor));
					}
				}
				else if(str.size())
				{
					UWARN("Cannot parse line \"%s\"", str.c_str());
				}
			}
		}

		UDEBUG("Time loading dictionary = %fs, dimension=%d", timer.ticks(), dimension);
	}
	else
	{
		UERROR("Cannot open dictionary file \"%s\"", dictionaryPath.c_str());
	}
	file.close();

	if(descriptors.size() && objectDescriptors.size() && dimension)
	{
		cv::Mat dataTree;
//.........这里部分代码省略.........