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Added parameter "Mem/UseOdometryFeatures=false" to use directly features already extracted from odometry for the vocabulary (issue #32)

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matlabbe
2016-02-22 17:57:40 -05:00
parent ccc4b4a6c2
commit d7ef170d33
15 changed files with 175 additions and 73 deletions
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1
corelib/include/rtabmap/core/Memory.h
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@@ -246,6 +246,7 @@ private:
float _rehearsalMaxAngle;
bool _rehearsalWeightIgnoredWhileMoving;
bool _useDepthAsMask;
bool _useOdometryFeatures;
int _idCount;
int _idMapCount;

4
corelib/include/rtabmap/core/Odometry.h
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@@ -54,7 +54,7 @@ public:
public:
virtual ~Odometry();
Transform process(const SensorData & data, OdometryInfo * info = 0);
Transform process(SensorData & data, OdometryInfo * info = 0);
virtual void reset(const Transform & initialPose = Transform::getIdentity());
//getters
@@ -63,7 +63,7 @@ public:
const Transform & previousTransform() const {return previousTransform_;}
private:
virtual Transform computeTransform(const SensorData & image, OdometryInfo * info = 0) = 0;
virtual Transform computeTransform(SensorData & data, OdometryInfo * info = 0) = 0;
void initKalmanFilter();
void updateKalmanFilter(float dt, float & x, float & y, float & z, float & roll, float & pitch, float & yaw);

2
corelib/include/rtabmap/core/OdometryF2F.h
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@@ -46,7 +46,7 @@ public:
const Signature & getRefFrame() const {return refFrame_;}
private:
virtual Transform computeTransform(const SensorData & image, OdometryInfo * info = 0);
virtual Transform computeTransform(SensorData & image, OdometryInfo * info = 0);
private:
//Parameters:

2
corelib/include/rtabmap/core/OdometryF2M.h
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@@ -45,7 +45,7 @@ public:
const std::multimap<int, cv::Point3f> & getLocalMap() const;
private:
virtual Transform computeTransform(const SensorData & image, OdometryInfo * info = 0);
virtual Transform computeTransform(SensorData & data, OdometryInfo * info = 0);
private:
//Parameters

2
corelib/include/rtabmap/core/OdometryMono.h
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@@ -43,7 +43,7 @@ public:
virtual void reset(const Transform & initialPose);
private:
virtual Transform computeTransform(const SensorData & data, OdometryInfo * info = 0);
virtual Transform computeTransform(SensorData & data, OdometryInfo * info = 0);
private:
//Parameters:
int flowWinSize_;

3
corelib/include/rtabmap/core/Parameters.h
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@@ -209,6 +209,7 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(Mem, ImageDecimation, int, 1, "Image decimation (>=1) when creating a signature.");
RTABMAP_PARAM(Mem, LaserScanDownsampleStepSize, int, 1, "If > 1, downsample the laser scans when creating a signature.");
RTABMAP_PARAM(Mem, UseDepthAsMask, bool, false, "Use depth image as mask for features detection.");
RTABMAP_PARAM(Mem, UseOdomFeatures, bool, false, "Use odometry features.");
// KeypointMemory (Keypoint-based)
RTABMAP_PARAM(Kp, NNStrategy, int, 1, "kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4");
@@ -269,7 +270,7 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(GFTT, K, double, 0.04, "");
RTABMAP_PARAM(ORB, ScaleFactor, float, 1.2, "Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.");
RTABMAP_PARAM(ORB, NLevels, int, 1, "The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).");
RTABMAP_PARAM(ORB, NLevels, int, 8, "The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).");
RTABMAP_PARAM(ORB, EdgeThreshold, int, 31, "This is size of the border where the features are not detected. It should roughly match the patchSize parameter.");
RTABMAP_PARAM(ORB, FirstLevel, int, 0, "It should be 0 in the current implementation.");
RTABMAP_PARAM(ORB, WTA_K, int, 2, "The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).");