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This commit is contained in:
matlabbe
2026-05-16 21:11:02 -07:00
parent f6ba3e6d7d
commit f40ea246f5
15 changed files with 397 additions and 92 deletions
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corelib/src/BayesFilter.cpp
+4 -1
View File
@@ -420,7 +420,10 @@ unsigned long BayesFilter::getMemoryUsed() const
{
long memoryUsage = sizeof(BayesFilter);
memoryUsage += _posterior.size() * (sizeof(float)+sizeof(int)+sizeof(std::map<int, float>::iterator)) + sizeof(std::map<int, float>);
memoryUsage += _prediction.total() * _prediction.elemSize();
if(!_prediction.empty())
{
memoryUsage += _prediction.total() * _prediction.elemSize();
}
memoryUsage += _predictionLC.size() * sizeof(double);
memoryUsage += _neighborsIndex.size() * (sizeof(int)+sizeof(std::map<int, int>)+sizeof(std::map<int, std::map<int, int> >::iterator)) + sizeof(std::map<int, std::map<int, int> >);
for(std::map<int, std::map<int, int> >::const_iterator iter=_neighborsIndex.begin(); iter!=_neighborsIndex.end(); ++iter)
corelib/src/Compression.cpp
+4
View File
@@ -331,6 +331,10 @@ std::string uncompressString(const cv::Mat & bytes)
std::string compressedDepthFormat(const cv::Mat & bytes)
{
if(bytes.empty())
{
return std::string();
}
return compressedDepthFormat(bytes.data, bytes.rows * bytes.cols * bytes.elemSize());
}
std::string compressedDepthFormat(const std::vector<unsigned char> & bytes)
corelib/src/SensorData.cpp
+33 -15
View File
@@ -36,6 +36,23 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
namespace rtabmap
{
namespace {
bool isOccupancyGridLayerFormatSupported(const cv::Mat & layer)
{
if(layer.empty())
{
return true;
}
return layer.type() == CV_32FC2 ||
layer.type() == CV_32FC3 ||
layer.type() == CV_32FC(4) ||
layer.type() == CV_32FC(5) ||
layer.type() == CV_32FC(6) ||
layer.type() == CV_32FC(7) ||
(layer.type() == CV_8UC1 && layer.rows == 1);
}
} // namespace
// empty constructor
SensorData::SensorData() :
_id(0),
@@ -582,6 +599,19 @@ void SensorData::setOccupancyGrid(
_emptyCellsRaw = cv::Mat();
_emptyCellsCompressed = cv::Mat();
if(!ground.empty() && !isOccupancyGridLayerFormatSupported(ground))
{
UFATAL("Unsupported local occupancy grid format for ground cells: OpenCV type=%d size=%dx%d", ground.type(), ground.cols, ground.rows);
}
if(!obstacles.empty() && !isOccupancyGridLayerFormatSupported(obstacles))
{
UFATAL("Unsupported local occupancy grid format for obstacle cells: OpenCV type=%d size=%dx%d", obstacles.type(), obstacles.cols, obstacles.rows);
}
if(!empty.empty() && !isOccupancyGridLayerFormatSupported(empty))
{
UFATAL("Unsupported local occupancy grid format for empty cells: OpenCV type=%d size=%dx%d", empty.type(), empty.cols, empty.rows);
}
CompressionThread ctGround(ground);
CompressionThread ctObstacles(obstacles);
CompressionThread ctEmpty(empty);
@@ -593,14 +623,10 @@ void SensorData::setOccupancyGrid(
_groundCellsRaw = ground;
ctGround.start();
}
else if(ground.type() == CV_8UC1 && ground.rows == 1)
else // CV_8UC1 && rows == 1
{
_groundCellsCompressed = ground;
}
else
{
UFATAL("Unsupported local occupancy grid format for ground cells: OpenCV type=%d size=%dx%d", ground.type(), ground.cols, ground.rows);
}
}
if(!obstacles.empty())
{
@@ -609,14 +635,10 @@ void SensorData::setOccupancyGrid(
_obstacleCellsRaw = obstacles;
ctObstacles.start();
}
else if(obstacles.type() == CV_8UC1 && obstacles.rows == 1)
else // CV_8UC1 && rows == 1
{
_obstacleCellsCompressed = obstacles;
}
else
{
UFATAL("Unsupported local occupancy grid format for obstacle cells: OpenCV type=%d size=%dx%d", obstacles.type(), obstacles.cols, obstacles.rows);
}
}
if(!empty.empty())
{
@@ -625,14 +647,10 @@ void SensorData::setOccupancyGrid(
_emptyCellsRaw = empty;
ctEmpty.start();
}
else if(empty.type() == CV_8UC1 && empty.rows == 1)
else // CV_8UC1 && rows == 1
{
_emptyCellsCompressed = empty;
}
else
{
UFATAL("Unsupported local occupancy grid format for empty cells: OpenCV type=%d size=%dx%d", empty.type(), empty.cols, empty.rows);
}
}
ctGround.join();
ctObstacles.join();
corelib/src/VWDictionary.cpp
+4 -1
View File
@@ -377,7 +377,10 @@ unsigned long VWDictionary::getMemoryUsed() const
{
long memoryUsage = sizeof(VWDictionary);
memoryUsage += getIndexMemoryUsed();
memoryUsage += _dataTree.total()*_dataTree.elemSize();
if(!_dataTree.empty())
{
memoryUsage += _dataTree.total()*_dataTree.elemSize();
}
if(!_visualWords.empty())
{
memoryUsage += _visualWords.size()*(sizeof(int) + _visualWords.rbegin()->second->getMemoryUsed() + sizeof(std::map<int, VisualWord *>::iterator)) + sizeof(std::map<int, VisualWord *>);
corelib/src/util3d_surface.cpp
+28 -10
View File
@@ -47,6 +47,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <pcl/surface/mls.h>
#include <pcl18/surface/texture_mapping.h>
#include <pcl/features/integral_image_normal.h>
#include <algorithm>
#ifdef RTABMAP_ALICE_VISION
#include <aliceVision/sfmData/SfMData.hpp>
@@ -91,6 +92,23 @@ namespace rtabmap
namespace util3d
{
namespace {
float pcaEigenvalueAt(const cv::Mat & eigenvalues, int index)
{
if(eigenvalues.empty())
{
return 0.f;
}
if(eigenvalues.rows == 1)
{
index = std::min(index, eigenvalues.cols - 1);
return eigenvalues.at<float>(0, index);
}
index = std::min(index, eigenvalues.rows - 1);
return eigenvalues.at<float>(index, 0);
}
} // namespace
void createPolygonIndexes(
const std::vector<pcl::Vertices> & polygons,
int cloudSize,
@@ -3218,7 +3236,7 @@ float computeNormalsComplexity(
}
if(oi>1)
{
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi*2)), cv::Mat(), CV_PCA_DATA_AS_ROW);
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi)), cv::Mat(), CV_PCA_DATA_AS_ROW);
if(pcaEigenVectors)
{
@@ -3230,7 +3248,7 @@ float computeNormalsComplexity(
}
UASSERT((is2d && pca_analysis.eigenvalues.total()>=2) || (!is2d && pca_analysis.eigenvalues.total()>=3));
// Get last eigen value, scale between 0 and 1: 0=low complexity, 1=high complexity
return pca_analysis.eigenvalues.at<float>(0, is2d?1:2)*(is2d?2.0f:3.0f);
return pcaEigenvalueAt(pca_analysis.eigenvalues, is2d?1:2)*(is2d?2.0f:3.0f);
}
}
else if(!scan.isEmpty())
@@ -3279,7 +3297,7 @@ float computeNormalsComplexity(
}
if(oi>1)
{
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi*2)), cv::Mat(), CV_PCA_DATA_AS_ROW);
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi)), cv::Mat(), CV_PCA_DATA_AS_ROW);
if(pcaEigenVectors)
{
@@ -3291,7 +3309,7 @@ float computeNormalsComplexity(
}
// Get last eigen value, scale between 0 and 1: 0=low complexity, 1=high complexity
return pca_analysis.eigenvalues.at<float>(0, is2d?1:2)*(is2d?2.0f:3.0f);
return pcaEigenvalueAt(pca_analysis.eigenvalues, is2d?1:2)*(is2d?2.0f:3.0f);
}
return 0.0f;
}
@@ -3335,7 +3353,7 @@ float computeNormalsComplexity(
}
if(oi>1)
{
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi*2)), cv::Mat(), CV_PCA_DATA_AS_ROW);
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi)), cv::Mat(), CV_PCA_DATA_AS_ROW);
if(pcaEigenVectors)
{
@@ -3347,7 +3365,7 @@ float computeNormalsComplexity(
}
// Get last eigen value, scale between 0 and 1: 0=low complexity, 1=high complexity
return pca_analysis.eigenvalues.at<float>(0, is2d?1:2)*(is2d?2.0f:3.0f);
return pcaEigenvalueAt(pca_analysis.eigenvalues, is2d?1:2)*(is2d?2.0f:3.0f);
}
return 0.0f;
}
@@ -3391,7 +3409,7 @@ float computeNormalsComplexity(
}
if(oi>1)
{
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi*2)), cv::Mat(), CV_PCA_DATA_AS_ROW);
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi)), cv::Mat(), CV_PCA_DATA_AS_ROW);
if(pcaEigenVectors)
{
@@ -3403,7 +3421,7 @@ float computeNormalsComplexity(
}
// Get last eigen value, scale between 0 and 1: 0=low complexity, 1=high complexity
return pca_analysis.eigenvalues.at<float>(0, is2d?1:2)*(is2d?2.0f:3.0f);
return pcaEigenvalueAt(pca_analysis.eigenvalues, is2d?1:2)*(is2d?2.0f:3.0f);
}
return 0.0f;
}
@@ -3447,7 +3465,7 @@ float computeNormalsComplexity(
}
if(oi>1)
{
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi*2)), cv::Mat(), CV_PCA_DATA_AS_ROW);
cv::PCA pca_analysis(cv::Mat(data_normals, cv::Range(0, oi)), cv::Mat(), CV_PCA_DATA_AS_ROW);
if(pcaEigenVectors)
{
@@ -3459,7 +3477,7 @@ float computeNormalsComplexity(
}
// Get last eigen value, scale between 0 and 1: 0=low complexity, 1=high complexity
return pca_analysis.eigenvalues.at<float>(0, is2d?1:2)*(is2d?2.0f:3.0f);
return pcaEigenvalueAt(pca_analysis.eigenvalues, is2d?1:2)*(is2d?2.0f:3.0f);
}
return 0.0f;
}