OdometryDVO.cpp

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/*
Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.
 
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    * Redistributions in binary form must reproduce the above copyright
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      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.
 
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#include "rtabmap/core/odometry/OdometryDVO.h"
#include "rtabmap/core/OdometryInfo.h"
#include "rtabmap/core/util2d.h"
#include "rtabmap/utilite/ULogger.h"
#include "rtabmap/utilite/UTimer.h"
#include "rtabmap/utilite/UStl.h"
#include <opencv2/imgproc/types_c.h>
 
#ifdef RTABMAP_DVO
#include <dvo/dense_tracking.h>
#include <dvo/core/surface_pyramid.h>
#include <dvo/core/rgbd_image.h>
#endif
 
namespace rtabmap {
 
OdometryDVO::OdometryDVO(const ParametersMap & parameters) :
        Odometry(parameters),
#ifdef RTABMAP_DVO
        dvo_(0),
        reference_(0),
        camera_(0),
        lost_(false),
#endif
        motionFromKeyFrame_(Transform::getIdentity())
{
}
 
OdometryDVO::~OdometryDVO()
{
#ifdef RTABMAP_DVO
        delete dvo_;
        delete reference_;
        delete camera_;
#endif
}
 
void OdometryDVO::reset(const Transform & initialPose)
{
        Odometry::reset(initialPose);
#ifdef RTABMAP_DVO
        if(dvo_)
        {
                delete dvo_;
                dvo_ = 0;
        }
        if(reference_)
        {
                delete reference_;
                reference_ = 0;
        }
        if(camera_)
        {
                delete camera_;
                camera_ = 0;
        }
        lost_ = false;
        motionFromKeyFrame_.setIdentity();
        previousLocalTransform_.setNull();
#endif
}
 
// return not null transform if odometry is correctly computed
Transform OdometryDVO::computeTransform(
                SensorData & data,
                const Transform & guess,
                OdometryInfo * info)
{
        Transform t;
 
#ifdef RTABMAP_DVO
        UTimer timer;
 
        if(data.imageRaw().empty() ||
                data.imageRaw().rows != data.depthOrRightRaw().rows ||
                data.imageRaw().cols != data.depthOrRightRaw().cols)
        {
                UERROR("Not supported input!");
                return t;
        }
 
        if(!(data.cameraModels().size() == 1 && data.cameraModels()[0].isValidForReprojection()))
        {
                UERROR("Invalid camera model! Only single RGB-D camera supported by DVO. Try another odometry approach.");
                return t;
        }
 
        if(dvo_ == 0)
        {
                dvo::DenseTracker::Config cfg = dvo::DenseTracker::getDefaultConfig();
 
                dvo_ = new dvo::DenseTracker(cfg);
        }
 
        cv::Mat grey, grey_s16, depth_inpainted, depth_mask, depth_mono, depth_float;
        if(data.imageRaw().type() != CV_32FC1)
        {
                if(data.imageRaw().type() == CV_8UC3)
                {
                        cv::cvtColor(data.imageRaw(), grey, CV_BGR2GRAY);
                }
                else
                {
                        grey = data.imageRaw();
                }
 
                grey.convertTo(grey_s16, CV_32F);
        }
        else
        {
                grey_s16 = data.imageRaw();
        }
 
        // make sure all zeros are NAN
        if(data.depthRaw().type() == CV_32FC1)
        {
                depth_float = data.depthRaw();
                for(int i=0; i<depth_float.rows; ++i)
                {
                        for(int j=0; j<depth_float.cols; ++j)
                        {
                                float & d = depth_float.at<float>(i,j);
                                if(d == 0.0f)
                                {
                                        d = NAN;
                                }
                        }
                }
        }
        else if(data.depthRaw().type() == CV_16UC1)
        {
                depth_float = cv::Mat(data.depthRaw().size(), CV_32FC1);
                for(int i=0; i<data.depthRaw().rows; ++i)
                {
                        for(int j=0; j<data.depthRaw().cols; ++j)
                        {
                                float d = float(data.depthRaw().at<unsigned short>(i,j))/1000.0f;
                                depth_float.at<float>(i, j) = d==0.0f?NAN:d;
                        }
                }
        }
        else
        {
                UFATAL("Unknown depth format!");
        }
 
        if(camera_ == 0)
        {
                dvo::core::IntrinsicMatrix intrinsics = dvo::core::IntrinsicMatrix::create(
                                                data.cameraModels()[0].fx(),
                                                data.cameraModels()[0].fy(),
                                                data.cameraModels()[0].cx(),
                                                data.cameraModels()[0].cy());
                camera_ = new dvo::core::RgbdCameraPyramid(
                                data.cameraModels()[0].imageWidth(),
                                data.cameraModels()[0].imageHeight(),
                                intrinsics);
        }
 
        dvo::core::RgbdImagePyramid * current = new dvo::core::RgbdImagePyramid(*camera_, grey_s16, depth_float);
 
        const Transform & localTransform = data.cameraModels()[0].localTransform();
        cv::Mat covariance;
        if(reference_ == 0)
        {
                reference_ = current;
                if(!lost_)
                {
                        t.setIdentity();
                }
                covariance = cv::Mat::eye(6,6,CV_64FC1) * 9999.0;
        }
        else
        {
                dvo::DenseTracker::Result result;
                dvo_->match(*reference_, *current, result);
                t = Transform::fromEigen3d(result.Transformation);
 
                if(result.Information(0,0) > 0.0 && result.Information(0,0) != 1.0)
                {
                        lost_ = false;
                        cv::Mat information = cv::Mat::eye(6,6, CV_64FC1);
                        memcpy(information.data, result.Information.data(), 36*sizeof(double));
                        //copy only diagonal to avoid g2o/gtsam errors on graph optimization
                        covariance  = cv::Mat::eye(6,6,CV_64FC1);
                        covariance = information.inv().mul(covariance);
                        //covariance *= 100.0; // to be in the same scale than loop closure detection
 
                        Transform currentMotion = t;
                        t = motionFromKeyFrame_.inverse() * t;
 
                        // TODO make parameters?
                        if(currentMotion.getNorm() > 0.01 || currentMotion.getAngle() > 0.01)
                        {
                                if(info)
                                {
                                        info->keyFrameAdded = true;
                                }
                                // new keyframe
                                delete reference_;
                                reference_ = current;
                                motionFromKeyFrame_.setIdentity();
                        }
                        else
                        {
                                delete current;
                                motionFromKeyFrame_ = currentMotion;
                        }
                }
                else
                {
                        lost_ = true;
                        delete reference_;
                        delete current;
                        reference_ = 0; // this will make restart from the next frame
                        motionFromKeyFrame_.setIdentity();
                        t.setNull();
                        previousLocalTransform_.setNull();
                        covariance = cv::Mat::eye(6,6,CV_64FC1) * 9999.0;
                        UWARN("dvo failed to estimate motion, tracking will be reinitialized on next frame.");
                }
 
                if(!t.isNull() && !t.isIdentity() && !localTransform.isIdentity() && !localTransform.isNull())
                {
                        // from camera frame to base frame
                        if(!previousLocalTransform_.isNull())
                        {
                                t = previousLocalTransform_ * t * localTransform.inverse();
                        }
                        else
                        {
                                t = localTransform * t * localTransform.inverse();
                        }
                        previousLocalTransform_ = localTransform;
                }
        }
 
        if(info)
        {
                info->type = (int)kTypeDVO;
                info->reg.covariance = covariance;
        }
 
        UINFO("Odom update time = %fs", timer.elapsed());
 
#else
        UERROR("RTAB-Map is not built with DVO support! Select another visual odometry approach.");
#endif
        return t;
}
 
} // namespace rtabmap