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Merge branch 'master' of github.com:introlab/rtabmap into gtest

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matlabbe
2026-05-26 20:50:54 -07:00
parent 919990ea1f 4756474367
commit fbea556380
23 changed files with 3561 additions and 2136 deletions
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corelib/include/rtabmap/core/MarkerDetector.h
+13 -3
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@@ -57,6 +57,12 @@ private:
};
class RTABMAP_CORE_EXPORT MarkerDetector {
public:
enum Strategy {
kStrategyOpencv,
kStrategyApriltag
};
public:
MarkerDetector(const ParametersMap & parameters = ParametersMap());
@@ -84,15 +90,19 @@ public:
cv::Mat * imageWithDetections = 0);
private:
#ifdef HAVE_OPENCV_ARUCO
cv::Ptr<cv::aruco::DetectorParameters> detectorParams_;
float markerLength_;
Strategy strategy_;
float markerLength_;
std::map<int, float> markerLengths_;
float maxDepthError_;
float maxRange_;
float minRange_;
int dictionaryId_;
#ifdef HAVE_OPENCV_ARUCO
cv::Ptr<cv::aruco::DetectorParameters> detectorParams_;
cv::Ptr<cv::aruco::Dictionary> dictionary_;
#endif
void * apriltagLibDetector_;
void * apriltagLibFamily_;
};
} /* namespace rtabmap */
corelib/include/rtabmap/core/Parameters.h
+14 -4
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@@ -933,19 +933,29 @@ class RTABMAP_CORE_EXPORT Parameters
RTABMAP_PARAM(GridGlobal, ProbClampingMax, float, 0.971, "Probability clamping maximum (value between 0 and 1).");
RTABMAP_PARAM(GridGlobal, FloodFillDepth, unsigned int, 0, "Flood fill filter (0=disabled), used to remove empty cells outside the map. The flood fill is done at the specified depth (between 1 and 16) of the OctoMap.");
RTABMAP_PARAM(Marker, Dictionary, int, 0, "Dictionary to use: DICT_ARUCO_4X4_50=0, DICT_ARUCO_4X4_100=1, DICT_ARUCO_4X4_250=2, DICT_ARUCO_4X4_1000=3, DICT_ARUCO_5X5_50=4, DICT_ARUCO_5X5_100=5, DICT_ARUCO_5X5_250=6, DICT_ARUCO_5X5_1000=7, DICT_ARUCO_6X6_50=8, DICT_ARUCO_6X6_100=9, DICT_ARUCO_6X6_250=10, DICT_ARUCO_6X6_1000=11, DICT_ARUCO_7X7_50=12, DICT_ARUCO_7X7_100=13, DICT_ARUCO_7X7_250=14, DICT_ARUCO_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16, DICT_APRILTAG_16h5=17, DICT_APRILTAG_25h9=18, DICT_APRILTAG_36h10=19, DICT_APRILTAG_36h11=20");
RTABMAP_PARAM(Marker, Length, float, 0, "The length (m) of the markers' side. 0 means automatic marker length estimation using the depth image (the camera should look at the marker perpendicularly for initialization).");
RTABMAP_PARAM(Marker, Strategy, int, 0, "Marker detection implementation: 0=OpenCV, 1=AprilTag");
RTABMAP_PARAM(Marker, Dictionary, int, 0, "Dictionary to use: DICT_ARUCO_4X4_50=0, DICT_ARUCO_4X4_100=1, DICT_ARUCO_4X4_250=2, DICT_ARUCO_4X4_1000=3, DICT_ARUCO_5X5_50=4, DICT_ARUCO_5X5_100=5, DICT_ARUCO_5X5_250=6, DICT_ARUCO_5X5_1000=7, DICT_ARUCO_6X6_50=8, DICT_ARUCO_6X6_100=9, DICT_ARUCO_6X6_250=10, DICT_ARUCO_6X6_1000=11, DICT_ARUCO_7X7_50=12, DICT_ARUCO_7X7_100=13, DICT_ARUCO_7X7_250=14, DICT_ARUCO_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16, DICT_APRILTAG_16h5=17, DICT_APRILTAG_25h9=18, DICT_APRILTAG_36h10=19, DICT_APRILTAG_36h11=20, DICT_ARUCO_MIP_36H12=21");
RTABMAP_PARAM(Marker, Length, float, 0, "The length (m) of the markers' side. Value <=0 means automatic marker length estimation using the depth image (the camera should look at the marker perpendicularly for initialization). If 0, the length is estimated only on the first marker detected, then re-used for all next detections (i.e., this assumes that markers have all the same length). With <0, the length is estimated once for each unique marker, then re-used for next detections with the same marker ID.");
RTABMAP_PARAM_STR(Marker, Lengths, "", uFormat("List of markers to detect. Format is the marker's ID followed by its length (in meters), multiple markers are separated by a vertical line (\"id1 length|id2 length\"). We can also define a range of markers with \"id1:id2 length\" (id2 included). If empty, all markers of the chosen dictionary can be detected and their length is set/estimated based on %s. For example, to detect markers 12 and 14 with lengths of 8 and 15 cm respectively, and all markers between 30 and 40 with a length of 10 cm, set \"12 0.08|14 0.15|30:40 0.1\".", kMarkerLength().c_str()).c_str());
RTABMAP_PARAM(Marker, MaxDepthError, float, 0.01, uFormat("Maximum depth error between all corners of a marker when estimating the marker length (when %s is 0). The smaller it is, the more perpendicular the camera should be toward the marker to initialize the length.", kMarkerLength().c_str()));
RTABMAP_PARAM(Marker, VarianceLinear, float, 0.001, uFormat("Linear variance to set on marker detections. If %s is enabled and %s=2 (GTSAM): it is the variance of the range factor, with 9999 to disable range factor and to do only bearing.", kMarkerVarianceOrientationIgnored().c_str(), kOptimizerStrategy().c_str()));
RTABMAP_PARAM(Marker, VarianceAngular, float, 0.01, uFormat("Angular variance to set on marker detections. If %s is enabled, it is ignored with %s=1 (g2o) and it corresponds to bearing variance with %s=2 (GTSAM).", kMarkerVarianceOrientationIgnored().c_str(), kOptimizerStrategy().c_str(), kOptimizerStrategy().c_str()));
RTABMAP_PARAM(Marker, VarianceOrientationIgnored, bool, false, uFormat("When this setting is false, the landmark's orientation is optimized during graph optimization. When this setting is true, only the position of the landmark is optimized. This can be useful when the landmark's orientation estimation is not reliable. Note that for %s=1 (g2o), only %s needs be set if we ignore orientation. For %s=2 (GTSAM), instead of optimizing the landmark's position directly, a bearing/range factor is used, with %s as the variance of the range factor (with 9999 to optimize the position with only a bearing factor) and %s as the variance of the bearing factor (pitch/yaw).", kOptimizerStrategy().c_str(), kMarkerVarianceLinear().c_str(), kOptimizerStrategy().c_str(), kMarkerVarianceLinear().c_str(), kMarkerVarianceAngular().c_str()));
RTABMAP_PARAM(Marker, CornerRefinementMethod, int, 0, "Corner refinement method (0: None, 1: Subpixel, 2:contour, 3: AprilTag2). For OpenCV <3.3.0, this is \"doCornerRefinement\" parameter: set 0 for false and 1 for true.");
RTABMAP_PARAM(Marker, MaxRange, float, 0.0, "Maximum range in which markers will be detected. <=0 for unlimited range.");
RTABMAP_PARAM(Marker, MinRange, float, 0.0, "Miniminum range in which markers will be detected. <=0 for unlimited range.");
RTABMAP_PARAM_STR(Marker, Priors, "", "World prior locations of the markers. The map will be transformed in marker's world frame when a tag is detected. Format is the marker's ID followed by its position (angles in rad), markers are separated by vertical line (\"id1 x y z roll pitch yaw|id2 x y z roll pitch yaw\"). Example: \"1 0 0 1 0 0 0|2 1 0 1 0 0 1.57\" (marker 2 is 1 meter forward than marker 1 with 90 deg yaw rotation).");
RTABMAP_PARAM_STR(Marker, Priors, "", "World prior locations of the markers. The map will be transformed in marker's world frame when a tag is detected. Format is the marker's ID followed by its position (angles in rad), multiple markers are separated by vertical line (\"id1 x y z roll pitch yaw|id2 x y z roll pitch yaw\"). Example: \"1 0 0 1 0 0 0|2 1 0 1 0 0 1.57\" (marker 2 is 1 meter forward than marker 1 with 90 deg yaw rotation).");
RTABMAP_PARAM(Marker, PriorsVarianceLinear, float, 0.001, "Linear variance to set on marker priors.");
RTABMAP_PARAM(Marker, PriorsVarianceAngular, float, 0.001, "Angular variance to set on marker priors.");
RTABMAP_PARAM(MarkerAprilTag, NThreads, int, 1, "How many threads should be used?");
RTABMAP_PARAM(MarkerAprilTag, QuadDecimate, float, 1.0, "Detection of quads can be done on a lower-resolution image, improving speed at a cost of pose accuracy and a slight decrease in detection rate. Decoding the binary payload is still done at full resolution.");
RTABMAP_PARAM(MarkerAprilTag, QuadSigma, float, 0.0, "What Gaussian blur should be applied to the segmented image (used for quad detection?) Parameter is the standard deviation in pixels. Very noisy images benefit from non-zero values (e.g. 0.8).");
RTABMAP_PARAM(MarkerAprilTag, RefineEdges, bool, true, uFormat("When true, the edges of the each quad are adjusted to \"snap to\" strong gradients nearby. This is useful when decimation is employed, as it can increase the quality of the initial quad estimate substantially. Generally recommended to be on (true). Very computationally inexpensive. Option is ignored if %s = 1.", kMarkerAprilTagQuadDecimate().c_str()));
RTABMAP_PARAM(MarkerAprilTag, DecodeSharpening, double, 0.25, "How much sharpening should be done to decoded images? This can help decode small tags but may or may not help in odd lighting conditions or low light conditions.");
RTABMAP_PARAM(MarkerAprilTag, Debug, bool, false, uFormat("When true, write a variety of debugging images to the working directory where the app started (not %s) at various stages through the detection process. (Somewhat slow).", kRtabmapWorkingDirectory().c_str()));
RTABMAP_PARAM(MarkerOpenCV, CornerRefinementMethod, int, 0, "Corner refinement method for OpenCV strategy (0: None, 1: Subpixel, 2:contour, 3: AprilTag2). For OpenCV <3.3.0, this is \"doCornerRefinement\" parameter: set 0 for false and 1 for true.");
RTABMAP_PARAM(ImuFilter, MadgwickGain, double, 0.1, "Gain of the filter. Higher values lead to faster convergence but more noise. Lower values lead to slower convergence but smoother signal, belongs in [0, 1].");
RTABMAP_PARAM(ImuFilter, MadgwickZeta, double, 0.0, "Gyro drift gain (approx. rad/s), belongs in [-1, 1].");
corelib/include/rtabmap/core/optimizer/OptimizerG2O.h
+6
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@@ -41,6 +41,12 @@ public:
static bool isCSparseAvailable();
static bool isCholmodAvailable();
public:
static bool loadGraph(
const std::string & fileName,
std::map<int, Transform> & poses,
std::multimap<int, Link> & edgeConstraints);
public:
OptimizerG2O(const ParametersMap & parameters = ParametersMap());
virtual ~OptimizerG2O() {}
corelib/include/rtabmap/core/optimizer/OptimizerGTSAM.h
+1
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@@ -77,6 +77,7 @@ private:
std::vector<ConstraintToFactor> lastAddedConstraints_;
int lastSwitchId_;
std::set<int> addedPoses_;
std::map<int, bool> isLandmarkWithRotation_; // persists across iSAM2 incremental calls
std::pair<int, std::uint64_t> lastRootFactorIndex_;
};