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msckf_vio/include/msckf_vio/feature.hpp

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/*
* COPYRIGHT AND PERMISSION NOTICE
* Penn Software MSCKF_VIO
* Copyright (C) 2017 The Trustees of the University of Pennsylvania
* All rights reserved.
*/
#ifndef MSCKF_VIO_FEATURE_H
#define MSCKF_VIO_FEATURE_H
#include <iostream>
#include <map>
#include <vector>
#include <Eigen/Dense>
#include <Eigen/Geometry>
#include <Eigen/StdVector>
#include "image_handler.h"
#include "math_utils.hpp"
#include "imu_state.h"
#include "cam_state.h"
namespace msckf_vio {
/*
* @brief Feature Salient part of an image. Please refer
* to the Appendix of "A Multi-State Constraint Kalman
* Filter for Vision-aided Inertial Navigation" for how
* the 3d position of a feature is initialized.
*/
struct Feature {
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
typedef long long int FeatureIDType;
/*
* @brief OptimizationConfig Configuration parameters
* for 3d feature position optimization.
*/
struct OptimizationConfig {
double translation_threshold;
double huber_epsilon;
double estimation_precision;
double initial_damping;
int outer_loop_max_iteration;
int inner_loop_max_iteration;
OptimizationConfig():
translation_threshold(0.2),
huber_epsilon(0.01),
estimation_precision(5e-7),
initial_damping(1e-3),
outer_loop_max_iteration(10),
inner_loop_max_iteration(10) {
return;
}
};
// Constructors for the struct.
Feature(): id(0), position(Eigen::Vector3d::Zero()),
is_initialized(false), is_anchored(false) {}
Feature(const FeatureIDType& new_id): id(new_id),
position(Eigen::Vector3d::Zero()),
is_initialized(false), is_anchored(false) {}
/*
* @brief cost Compute the cost of the camera observations
* @param T_c0_c1 A rigid body transformation takes
* a vector in c0 frame to ci frame.
* @param x The current estimation.
* @param z The ith measurement of the feature j in ci frame.
* @return e The cost of this observation.
*/
inline void cost(const Eigen::Isometry3d& T_c0_ci,
const Eigen::Vector3d& x, const Eigen::Vector2d& z,
double& e) const;
/*
* @brief jacobian Compute the Jacobian of the camera observation
* @param T_c0_c1 A rigid body transformation takes
* a vector in c0 frame to ci frame.
* @param x The current estimation.
* @param z The actual measurement of the feature in ci frame.
* @return J The computed Jacobian.
* @return r The computed residual.
* @return w Weight induced by huber kernel.
*/
inline void jacobian(const Eigen::Isometry3d& T_c0_ci,
const Eigen::Vector3d& x, const Eigen::Vector2d& z,
Eigen::Matrix<double, 2, 3>& J, Eigen::Vector2d& r,
double& w) const;
/*
* @brief generateInitialGuess Compute the initial guess of
* the feature's 3d position using only two views.
* @param T_c1_c2: A rigid body transformation taking
* a vector from c2 frame to c1 frame.
* @param z1: feature observation in c1 frame.
* @param z2: feature observation in c2 frame.
* @return p: Computed feature position in c1 frame.
*/
inline void generateInitialGuess(
const Eigen::Isometry3d& T_c1_c2, const Eigen::Vector2d& z1,
const Eigen::Vector2d& z2, Eigen::Vector3d& p) const;
/*
* @brief checkMotion Check the input camera poses to ensure
* there is enough translation to triangulate the feature
* positon.
* @param cam_states : input camera poses.
* @return True if the translation between the input camera
* poses is sufficient.
*/
inline bool checkMotion(
const CamStateServer& cam_states) const;
/*
* @brief InitializeAnchor generates the NxN patch around the
* feature in the Anchor image
* @param cam_states: A map containing all recorded images
* currently presented in the camera state vector
* @return the irradiance of the Anchor NxN Patch
* @return True if the Anchor can be estimated
*/
bool initializeAnchor(
const CameraCalibration& cam);
/*
* @brief InitializePosition Intialize the feature position
* based on all current available measurements.
* @param cam_states: A map containing the camera poses with its
* ID as the associated key value.
* @return The computed 3d position is used to set the position
* member variable. Note the resulted position is in world
* frame.
* @return True if the estimated 3d position of the feature
* is valid.
*/
inline bool initializePosition(
const CamStateServer& cam_states);
/*
* @brief project PositionToCamera Takes a 3d position in a world frame
* and projects it into the passed camera frame using pinhole projection
* then distorts it using camera information to get
* the resulting distorted pixel position
*/
inline cv::Point2f projectPositionToCamera(
const CAMState& cam_state,
const StateIDType& cam_state_id,
const CameraCalibration& cam,
Eigen::Vector3d& in_p) const;
/*
* @brief IrradianceAnchorPatch_Camera returns irradiance values
* of the Anchor Patch position in a camera frame
*
*/
bool estimate_FrameIrradiance(
const CAMState& cam_state,
const StateIDType& cam_state_id,
CameraCalibration& cam0,
std::vector<float>& anchorPatch_estimate) const;
bool FrameIrradiance(
const CAMState& cam_state,
const StateIDType& cam_state_id,
CameraCalibration& cam0,
std::vector<float>& anchorPatch_measurement) const;
/*
* @brief projectPixelToPosition uses the calcualted pixels
* of the anchor patch to generate 3D positions of all of em
*/
inline Eigen::Vector3d projectPixelToPosition(cv::Point2f in_p,
const CameraCalibration& cam);
/*
* @brief Irradiance returns irradiance value of a pixel
*/
inline float PixelIrradiance(cv::Point2f pose, cv::Mat image) const;
// An unique identifier for the feature.
// In case of long time running, the variable
// type of id is set to FeatureIDType in order
// to avoid duplication.
FeatureIDType id;
// id for next feature
static FeatureIDType next_id;
// Store the observations of the features in the
// state_id(key)-image_coordinates(value) manner.
std::map<StateIDType, Eigen::Vector4d, std::less<StateIDType>,
Eigen::aligned_allocator<
std::pair<const StateIDType, Eigen::Vector4d> > > observations;
// NxN Patch of Anchor Image
std::vector<double> anchorPatch;
// Position of NxN Patch in 3D space
std::vector<Eigen::Vector3d> anchorPatch_3d;
// Anchor Isometry
Eigen::Isometry3d T_anchor_w;
// 3d postion of the feature in the world frame.
Eigen::Vector3d position;
// inverse depth representation
double anchor_rho;
// A indicator to show if the 3d postion of the feature
// has been initialized or not.
bool is_initialized;
bool is_anchored;
cv::Point2f anchor_center_pos;
cv::Point2f undist_anchor_center_pos;
// Noise for a normalized feature measurement.
static double observation_noise;
// Optimization configuration for solving the 3d position.
static OptimizationConfig optimization_config;
};
typedef Feature::FeatureIDType FeatureIDType;
typedef std::map<FeatureIDType, Feature, std::less<int>,
Eigen::aligned_allocator<
std::pair<const FeatureIDType, Feature> > > MapServer;
void Feature::cost(const Eigen::Isometry3d& T_c0_ci,
const Eigen::Vector3d& x, const Eigen::Vector2d& z,
double& e) const {
// Compute hi1, hi2, and hi3 as Equation (37).
const double& alpha = x(0);
const double& beta = x(1);
const double& rho = x(2);
Eigen::Vector3d h = T_c0_ci.linear()*
Eigen::Vector3d(alpha, beta, 1.0) + rho*T_c0_ci.translation();
double& h1 = h(0);
double& h2 = h(1);
double& h3 = h(2);
// Predict the feature observation in ci frame.
Eigen::Vector2d z_hat(h1/h3, h2/h3);
// Compute the residual.
e = (z_hat-z).squaredNorm();
return;
}
void Feature::jacobian(const Eigen::Isometry3d& T_c0_ci,
const Eigen::Vector3d& x, const Eigen::Vector2d& z,
Eigen::Matrix<double, 2, 3>& J, Eigen::Vector2d& r,
double& w) const {
// Compute hi1, hi2, and hi3 as Equation (37).
const double& alpha = x(0);
const double& beta = x(1);
const double& rho = x(2);
Eigen::Vector3d h = T_c0_ci.linear()*
Eigen::Vector3d(alpha, beta, 1.0) + rho*T_c0_ci.translation();
double& h1 = h(0);
double& h2 = h(1);
double& h3 = h(2);
// Compute the Jacobian.
Eigen::Matrix3d W;
W.leftCols<2>() = T_c0_ci.linear().leftCols<2>();
W.rightCols<1>() = T_c0_ci.translation();
J.row(0) = 1/h3*W.row(0) - h1/(h3*h3)*W.row(2);
J.row(1) = 1/h3*W.row(1) - h2/(h3*h3)*W.row(2);
// Compute the residual.
Eigen::Vector2d z_hat(h1/h3, h2/h3);
r = z_hat - z;
// Compute the weight based on the residual.
double e = r.norm();
if (e <= optimization_config.huber_epsilon)
w = 1.0;
else
w = optimization_config.huber_epsilon / (2*e);
return;
}
void Feature::generateInitialGuess(
const Eigen::Isometry3d& T_c1_c2, const Eigen::Vector2d& z1,
const Eigen::Vector2d& z2, Eigen::Vector3d& p) const {
// Construct a least square problem to solve the depth.
Eigen::Vector3d m = T_c1_c2.linear() * Eigen::Vector3d(z1(0), z1(1), 1.0);
Eigen::Vector2d A(0.0, 0.0);
A(0) = m(0) - z2(0)*m(2);
A(1) = m(1) - z2(1)*m(2);
Eigen::Vector2d b(0.0, 0.0);
b(0) = z2(0)*T_c1_c2.translation()(2) - T_c1_c2.translation()(0);
b(1) = z2(1)*T_c1_c2.translation()(2) - T_c1_c2.translation()(1);
// Solve for the depth.
double depth = (A.transpose() * A).inverse() * A.transpose() * b;
p(0) = z1(0) * depth;
p(1) = z1(1) * depth;
p(2) = depth;
return;
}
bool Feature::checkMotion(
const CamStateServer& cam_states) const {
const StateIDType& first_cam_id = observations.begin()->first;
const StateIDType& last_cam_id = (--observations.end())->first;
Eigen::Isometry3d first_cam_pose;
first_cam_pose.linear() = quaternionToRotation(
cam_states.find(first_cam_id)->second.orientation).transpose();
first_cam_pose.translation() =
cam_states.find(first_cam_id)->second.position;
Eigen::Isometry3d last_cam_pose;
last_cam_pose.linear() = quaternionToRotation(
cam_states.find(last_cam_id)->second.orientation).transpose();
last_cam_pose.translation() =
cam_states.find(last_cam_id)->second.position;
// Get the direction of the feature when it is first observed.
// This direction is represented in the world frame.
Eigen::Vector3d feature_direction(
observations.begin()->second(0),
observations.begin()->second(1), 1.0);
feature_direction = feature_direction / feature_direction.norm();
feature_direction = first_cam_pose.linear()*feature_direction;
// Compute the translation between the first frame
// and the last frame. We assume the first frame and
// the last frame will provide the largest motion to
// speed up the checking process.
Eigen::Vector3d translation = last_cam_pose.translation() -
first_cam_pose.translation();
double parallel_translation =
translation.transpose()*feature_direction;
Eigen::Vector3d orthogonal_translation = translation -
parallel_translation*feature_direction;
if (orthogonal_translation.norm() >
optimization_config.translation_threshold)
return true;
else return false;
}
bool Feature::estimate_FrameIrradiance(
const CAMState& cam_state,
const StateIDType& cam_state_id,
CameraCalibration& cam0,
std::vector<float>& anchorPatch_estimate) const
{
// get irradiance of patch in anchor frame
// subtract estimated b and divide by a of anchor frame
// muliply by a and add b of this frame
auto anchor = observations.begin();
if(cam0.moving_window.find(anchor->first) == cam0.moving_window.end())
return false;
double anchorExposureTime_ms = cam0.moving_window.find(anchor->first)->second.exposureTime_ms;
double frameExposureTime_ms = cam0.moving_window.find(cam_state_id)->second.exposureTime_ms;
double a_A = anchorExposureTime_ms;
double b_A = 0;
double a_l =frameExposureTime_ms;
double b_l = 0;
//printf("frames: %lld, %lld\n", anchor->first, cam_state_id);
//printf("exposure: %f, %f\n", a_A, a_l);
for (double anchorPixel : anchorPatch)
{
float irradiance = ((anchorPixel - b_A) / a_A ) * a_l - b_l;
anchorPatch_estimate.push_back(irradiance);
}
}
bool Feature::FrameIrradiance(
const CAMState& cam_state,
const StateIDType& cam_state_id,
CameraCalibration& cam0,
std::vector<float>& anchorPatch_measurement) const
{
// visu - feature
/*cv::Mat current_image = cam0.moving_window.find(cam_state_id)->second.image;
cv::Mat dottedFrame(current_image.size(), CV_8UC3);
cv::cvtColor(current_image, dottedFrame, CV_GRAY2RGB);
*/
// project every point in anchorPatch_3d.
for (auto point : anchorPatch_3d)
{
cv::Point2f p_in_c0 = projectPositionToCamera(cam_state, cam_state_id, cam0, point);
// visu - feature
/*cv::Point xs(p_in_c0.x, p_in_c0.y);
cv::Point ys(p_in_c0.x, p_in_c0.y);
cv::rectangle(dottedFrame, xs, ys, cv::Scalar(0,255,0));
*/
float irradiance = PixelIrradiance(p_in_c0, cam0.moving_window.find(cam_state_id)->second.image);
anchorPatch_measurement.push_back(irradiance);
// testing
//if(cam_state_id == observations.begin()->first)
//if(count++ == 4)
//printf("dist:\n \tpos: %f, %f\n\ttrue pos: %f, %f\n\n", p_in_c0.x, p_in_c0.y, anchor_center_pos.x, anchor_center_pos.y);
}
// visu - feature
//cv::resize(dottedFrame, dottedFrame, cv::Size(dottedFrame.cols*0.2, dottedFrame.rows*0.2));
/*if(cam0.featureVisu.empty())
cam0.featureVisu = dottedFrame.clone();
else
cv::hconcat(cam0.featureVisu, dottedFrame, cam0.featureVisu);
*/
}
float Feature::PixelIrradiance(cv::Point2f pose, cv::Mat image) const
{
return (float)image.at<uint8_t>(pose.x, pose.y);
}
cv::Point2f Feature::projectPositionToCamera(
const CAMState& cam_state,
const StateIDType& cam_state_id,
const CameraCalibration& cam,
Eigen::Vector3d& in_p) const
{
Eigen::Isometry3d T_c0_w;
cv::Point2f out_p;
// transfrom position to camera frame
Eigen::Matrix3d R_w_c0 = quaternionToRotation(cam_state.orientation);
const Eigen::Vector3d& t_c0_w = cam_state.position;
Eigen::Vector3d p_c0 = R_w_c0 * (in_p-t_c0_w);
out_p = cv::Point2f(p_c0(0)/p_c0(2), p_c0(1)/p_c0(2));
// if(cam_state_id == observations.begin()->first)
//printf("undist:\n \tproj pos: %f, %f\n\ttrue pos: %f, %f\n", out_p.x, out_p.y, undist_anchor_center_pos.x, undist_anchor_center_pos.y);
cv::Point2f my_p = image_handler::distortPoint(out_p,
cam.intrinsics,
cam.distortion_model,
cam.distortion_coeffs);
// printf("truPosition: %f, %f, %f\n", position.x(), position.y(), position.z());
// printf("camPosition: %f, %f, %f\n", p_c0(0), p_c0(1), p_c0(2));
// printf("Photo projection: %f, %f\n", my_p[0].x, my_p[0].y);
return my_p;
}
Eigen::Vector3d Feature::projectPixelToPosition(cv::Point2f in_p,
const CameraCalibration& cam)
{
// use undistorted position of point of interest
// project it back into 3D space using pinhole model
// save resulting NxN positions for this feature
Eigen::Vector3d PositionInCamera(in_p.x/anchor_rho, in_p.y/anchor_rho, 1/anchor_rho);
Eigen::Vector3d PositionInWorld= T_anchor_w.linear()*PositionInCamera + T_anchor_w.translation();
return PositionInWorld;
//printf("%f, %f, %f\n",PositionInWorld[0], PositionInWorld[1], PositionInWorld[2]);
}
//@test center projection must always be initial feature projection
bool Feature::initializeAnchor(
const CameraCalibration& cam)
{
//initialize patch Size
//TODO make N size a ros parameter
int N = 3;
int n = (int)(N-1)/2;
auto anchor = observations.begin();
if(cam.moving_window.find(anchor->first) == cam.moving_window.end())
return false;
cv::Mat anchorImage = cam.moving_window.find(anchor->first)->second.image;
auto u = anchor->second(0);//*cam.intrinsics[0] + cam.intrinsics[2];
auto v = anchor->second(1);//*cam.intrinsics[1] + cam.intrinsics[3];
//testing
undist_anchor_center_pos = cv::Point2f(u,v);
//for NxN patch pixels around feature
int count = 0;
// get feature in undistorted pixel space
cv::Point2f und_pix_p = image_handler::distortPoint(cv::Point2f(u, v),
cam.intrinsics,
cam.distortion_model,
0);
// create vector of patch in pixel plane
std::vector<cv::Point2f> und_pix_v;
for(double u_run = -n; u_run <= n; u_run++)
for(double v_run = -n; v_run <= n; v_run++)
und_pix_v.push_back(cv::Point2f(und_pix_p.x-u_run, und_pix_p.y-v_run));
//create undistorted pure points
std::vector<cv::Point2f> und_v;
image_handler::undistortPoints(und_pix_v,
cam.intrinsics,
cam.distortion_model,
0,
und_v);
//create distorted pixel points
std::vector<cv::Point2f> vec = image_handler::distortPoints(und_v,
cam.intrinsics,
cam.distortion_model,
cam.distortion_coeffs);
// save anchor position for later visualisaztion
anchor_center_pos = vec[4];
for(auto point : vec)
{
if(point.x - n < 0 || point.x + n >= cam.resolution(0) || point.y - n < 0 || point.y + n >= cam.resolution(1))
return false;
}
for(auto point : vec)
anchorPatch.push_back((double)anchorImage.at<uint8_t>((int)point.x,(int)point.y));
// project patch pixel to 3D space
for(auto point : und_v)
anchorPatch_3d.push_back(projectPixelToPosition(point, cam));
is_anchored = true;
return true;
}
bool Feature::initializePosition(
const CamStateServer& cam_states) {
// Organize camera poses and feature observations properly.
std::vector<Eigen::Isometry3d,
Eigen::aligned_allocator<Eigen::Isometry3d> > cam_poses(0);
std::vector<Eigen::Vector2d,
Eigen::aligned_allocator<Eigen::Vector2d> > measurements(0);
for (auto& m : observations) {
// TODO: This should be handled properly. Normally, the
// required camera states should all be available in
// the input cam_states buffer.
auto cam_state_iter = cam_states.find(m.first);
if (cam_state_iter == cam_states.end()) continue;
// Add the measurement.
measurements.push_back(m.second.head<2>());
measurements.push_back(m.second.tail<2>());
// This camera pose will take a vector from this camera frame
// to the world frame.
Eigen::Isometry3d cam0_pose;
cam0_pose.linear() = quaternionToRotation(
cam_state_iter->second.orientation).transpose();
cam0_pose.translation() = cam_state_iter->second.position;
Eigen::Isometry3d cam1_pose;
cam1_pose = cam0_pose * CAMState::T_cam0_cam1.inverse();
cam_poses.push_back(cam0_pose);
cam_poses.push_back(cam1_pose);
}
// All camera poses should be modified such that it takes a
// vector from the first camera frame in the buffer to this
// camera frame.
Eigen::Isometry3d T_c0_w = cam_poses[0];
T_anchor_w = T_c0_w;
for (auto& pose : cam_poses)
pose = pose.inverse() * T_c0_w;
// Generate initial guess
Eigen::Vector3d initial_position(0.0, 0.0, 0.0);
generateInitialGuess(cam_poses[cam_poses.size()-1], measurements[0],
measurements[measurements.size()-1], initial_position);
Eigen::Vector3d solution(
initial_position(0)/initial_position(2),
initial_position(1)/initial_position(2),
1.0/initial_position(2));
// Apply Levenberg-Marquart method to solve for the 3d position.
double lambda = optimization_config.initial_damping;
int inner_loop_cntr = 0;
int outer_loop_cntr = 0;
bool is_cost_reduced = false;
double delta_norm = 0;
// Compute the initial cost.
double total_cost = 0.0;
for (int i = 0; i < cam_poses.size(); ++i) {
double this_cost = 0.0;
cost(cam_poses[i], solution, measurements[i], this_cost);
total_cost += this_cost;
}
// Outer loop.
do {
Eigen::Matrix3d A = Eigen::Matrix3d::Zero();
Eigen::Vector3d b = Eigen::Vector3d::Zero();
for (int i = 0; i < cam_poses.size(); ++i) {
Eigen::Matrix<double, 2, 3> J;
Eigen::Vector2d r;
double w;
jacobian(cam_poses[i], solution, measurements[i], J, r, w);
if (w == 1) {
A += J.transpose() * J;
b += J.transpose() * r;
} else {
double w_square = w * w;
A += w_square * J.transpose() * J;
b += w_square * J.transpose() * r;
}
}
// Inner loop.
// Solve for the delta that can reduce the total cost.
do {
Eigen::Matrix3d damper = lambda * Eigen::Matrix3d::Identity();
Eigen::Vector3d delta = (A+damper).ldlt().solve(b);
Eigen::Vector3d new_solution = solution - delta;
delta_norm = delta.norm();
double new_cost = 0.0;
for (int i = 0; i < cam_poses.size(); ++i) {
double this_cost = 0.0;
cost(cam_poses[i], new_solution, measurements[i], this_cost);
new_cost += this_cost;
}
if (new_cost < total_cost) {
is_cost_reduced = true;
solution = new_solution;
total_cost = new_cost;
lambda = lambda/10 > 1e-10 ? lambda/10 : 1e-10;
} else {
is_cost_reduced = false;
lambda = lambda*10 < 1e12 ? lambda*10 : 1e12;
}
} while (inner_loop_cntr++ <
optimization_config.inner_loop_max_iteration && !is_cost_reduced);
inner_loop_cntr = 0;
} while (outer_loop_cntr++ <
optimization_config.outer_loop_max_iteration &&
delta_norm > optimization_config.estimation_precision);
// Covert the feature position from inverse depth
// representation to its 3d coordinate.
Eigen::Vector3d final_position(solution(0)/solution(2),
solution(1)/solution(2), 1.0/solution(2));
// Check if the solution is valid. Make sure the feature
// is in front of every camera frame observing it.
bool is_valid_solution = true;
for (const auto& pose : cam_poses) {
Eigen::Vector3d position =
pose.linear()*final_position + pose.translation();
if (position(2) <= 0) {
is_valid_solution = false;
break;
}
}
//save inverse depth distance from camera
anchor_rho = solution(2);
// Convert the feature position to the world frame.
position = T_c0_w.linear()*final_position + T_c0_w.translation();
if (is_valid_solution)
is_initialized = true;
return is_valid_solution;
}
} // namespace msckf_vio
#endif // MSCKF_VIO_FEATURE_H
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