文章目錄
SFND 3D Object Tracking
github:
https://github.com/bjtylxl/FND_P4_3D_Object_Tracking
Welcome to the final project of the camera course. By completing all the lessons, you now have a solid understanding of keypoint detectors, descriptors, and methods to match them between successive images. Also, you know how to detect objects in an image using the YOLO deep-learning framework. And finally, you know how to associate regions in a camera image with Lidar points in 3D space. Let’s take a look at our program schematic to see what we already have accomplished and what’s still missing.
In this final project, you will implement the missing parts in the schematic. To do this, you will complete four major tasks:
- First, you will develop a way to match 3D objects over time by using keypoint correspondences.
- Second, you will compute the TTC based on Lidar measurements.
- You will then proceed to do the same using the camera, which requires to first associate keypoint matches to regions of interest and then to compute the TTC based on those matches.
- And lastly, you will conduct various tests with the framework. Your goal is to identify the most suitable detector/descriptor combination for TTC estimation and also to search for problems that can lead to faulty measurements by the camera or Lidar sensor. In the last course of this Nanodegree, you will learn about the Kalman filter, which is a great way to combine the two independent TTC measurements into an improved version which is much more reliable than a single sensor alone can be. But before we think about such things, let us focus on your final project in the camera course.
Dependencies for Running Locally
- cmake >= 2.8
- All OSes: click here for installation instructions
- make >= 4.1 (Linux, Mac), 3.81 (Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
- OpenCV >= 4.1
- This must be compiled from source using the
-D OPENCV_ENABLE_NONFREE=ONcmake flag for testing the SIFT and SURF detectors. - The OpenCV 4.1.0 source code can be found here
- This must be compiled from source using the
- gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - install Xcode command line tools
- Windows: recommend using MinGW
Basic Build Instructions
- Clone this repo.
- Make a build directory in the top level project directory:
mkdir build && cd build - Compile:
cmake .. && make - Run it:
./3D_object_tracking.
Rubric Points
FP.1 : Match 3D Objects
In this task, please implement the method “matchBoundingBoxes”, which takes as input both the previous and the current d
void matchBoundingBoxes(std::vector<cv::DMatch> &matches, std::map<int, int> &bbBestMatches, DataFrame &prevFrame, DataFrame &currFrame)
{
int p = prevFrame.boundingBoxes.size();
int c = currFrame.boundingBoxes.size();
int pt_counts[p][c] = { };
for (auto it = matches.begin(); it != matches.end() - 1; ++it) {
cv::KeyPoint query = prevFrame.keypoints[it->queryIdx];
auto query_pt = cv::Point(query.pt.x, query.pt.y);
bool query_found = false;
cv::KeyPoint train = currFrame.keypoints[it->trainIdx];
auto train_pt = cv::Point(train.pt.x, train.pt.y);
bool train_found = false;
std::vector<int> query_id, train_id;
for (int i = 0; i < p; i++) {
if (prevFrame.boundingBoxes[i].roi.contains(query_pt)) {
query_found = true;
query_id.push_back(i);
}
}
for (int i = 0; i < c; i++) {
if (currFrame.boundingBoxes[i].roi.contains(train_pt)) {
train_found= true;
train_id.push_back(i);
}
}
if (query_found && train_found)
{
for (auto id_prev: query_id)
for (auto id_curr: train_id)
pt_counts[id_prev][id_curr] += 1;
}
}
for (int i = 0; i < p; i++)
{
int max_count = 0;
int id_max = 0;
for (int j = 0; j < c; j++)
if (pt_counts[i][j] > max_count)
{
max_count = pt_counts[i][j];
id_max = j;
}
bbBestMatches[i] = id_max;
}
bool bMsg = true;
if (bMsg)
for (int i = 0; i < p; i++)
cout << "Box " << i << " matches " << bbBestMatches[i]<< " box" << endl;
}
FP.2 : Compute Lidar-based TTC
In this part of the final project, your task is to compute the time-to-collision for all matched 3D objects based on Lidar measurements alone. Please take a look at the second lesson of this course to revisit the theory behind TTC estimation. Also, please implement the estimation in a way that makes it robust against outliers which might be way too close and thus lead to faulty estimates of the TTC. Please return your TCC to the main function at the end of computeTTCLidar.
void computeTTCLidar(std::vector<LidarPoint> &lidarPointsPrev,
std::vector<LidarPoint> &lidarPointsCurr, double frameRate, double &TTC)
{
double dT = 1 / frameRate;
double laneWidth = 4.0; // assumed width of the ego lane
vector<double> xPrev, xCurr;
// find Lidar points within ego lane
for (auto it = lidarPointsPrev.begin(); it != lidarPointsPrev.end(); ++it)
{
if (abs(it->y) <= laneWidth / 2.0)
{ // 3D point within ego lane?
xPrev.push_back(it->x);
}
}
for (auto it = lidarPointsCurr.begin(); it != lidarPointsCurr.end(); ++it)
{
if (abs(it->y) <= laneWidth / 2.0)
{ // 3D point within ego lane?
xCurr.push_back(it->x);
}
}
double minXPrev = 0;
double minXCurr = 0;
if (xPrev.size() > 0)
{
for (auto x: xPrev)
minXPrev += x;
minXPrev = minXPrev / xPrev.size();
}
if (xCurr.size() > 0)
{
for (auto x: xCurr)
minXCurr += x;
minXCurr = minXCurr / xCurr.size();
}
// compute TTC from both measurements
cout << "minXCurr: " << minXCurr << endl;
cout << "minXPrev: " << minXPrev << endl;
TTC = minXCurr * dT / (minXPrev - minXCurr);
}
FP.3 : Associate Keypoint Correspondences with Bounding Boxes
Before a TTC estimate can be computed in the next exercise, you need to find all keypoint matches that belong to each 3D object. You can do this by simply checking wether the corresponding keypoints are within the region of interest in the camera image. All matches which satisfy this condition should be added to a vector. The problem you will find is that there will be outliers among your matches. To eliminate those, I recommend that you compute a robust mean of all the euclidean distances between keypoint matches and then remove those that are too far away from the mean.
void clusterKptMatchesWithROI(BoundingBox &boundingBox, std::vector<cv::KeyPoint> &kptsPrev, std::vector<cv::KeyPoint> &kptsCurr, std::vector<cv::DMatch> &kptMatches)
{
// ...
double dist_mean = 0;
std::vector<cv::DMatch> kptMatches_roi;
for (auto it = kptMatches.begin(); it != kptMatches.end(); ++it)
{
cv::KeyPoint kp = kptsCurr.at(it->trainIdx);
if (boundingBox.roi.contains(cv::Point(kp.pt.x, kp.pt.y)))
kptMatches_roi.push_back(*it);
}
for (auto it = kptMatches_roi.begin(); it != kptMatches_roi.end(); ++it)
dist_mean += it->distance;
cout << "Find " << kptMatches_roi.size() << " matches" << endl;
if (kptMatches_roi.size() > 0)
dist_mean = dist_mean/kptMatches_roi.size();
else return;
double threshold = dist_mean * 0.7;
for (auto it = kptMatches_roi.begin(); it != kptMatches_roi.end(); ++it)
{
if (it->distance < threshold)
boundingBox.kptMatches.push_back(*it);
}
cout << "Leave " << boundingBox.kptMatches.size() << " matches" << endl;
}
FP.4 : Compute Camera-based TTC
Once keypoint matches have been added to the bounding boxes, the next step is to compute the TTC estimate. As with Lidar, we already looked into this in the second lesson of this course, so you please revisit the respective section and use the code sample there as a starting point for this task here. Once you have your estimate of the TTC, please return it to the main function at the end of computeTTCCamera.
void computeTTCCamera(std::vector<cv::KeyPoint> &kptsPrev, std::vector<cv::KeyPoint> &kptsCurr,
std::vector<cv::DMatch> kptMatches, double frameRate, double &TTC, cv::Mat *visImg)
{
vector<double> distRatios; // stores the distance ratios for all keypoints between curr. and prev. frame
for (auto it1 = kptMatches.begin(); it1 != kptMatches.end() - 1; ++it1)
{
cv::KeyPoint kpOuterCurr = kptsCurr.at(it1->trainIdx);
cv::KeyPoint kpOuterPrev = kptsPrev.at(it1->queryIdx);
for (auto it2 = kptMatches.begin() + 1; it2 != kptMatches.end(); ++it2)
{
double minDist = 100.0; // min. required distance
cv::KeyPoint kpInnerCurr = kptsCurr.at(it2->trainIdx);
cv::KeyPoint kpInnerPrev = kptsPrev.at(it2->queryIdx);
// compute distances and distance ratios
double distCurr = cv::norm(kpOuterCurr.pt - kpInnerCurr.pt);
double distPrev = cv::norm(kpOuterPrev.pt - kpInnerPrev.pt);
if (distPrev > std::numeric_limits<double>::epsilon() && distCurr >= minDist)
{ // avoid division by zero
double distRatio = distCurr / distPrev;
distRatios.push_back(distRatio);
}
}
}
// only continue if list of distance ratios is not empty
if (distRatios.size() == 0)
{
TTC = NAN;
return;
}
std::sort(distRatios.begin(), distRatios.end());
long medIndex = floor(distRatios.size() / 2.0);
double medDistRatio = distRatios.size() % 2 == 0 ? (distRatios[medIndex - 1] + distRatios[medIndex]) / 2.0 : distRatios[medIndex]; // compute median dist. ratio to remove outlier influence
double dT = 1 / frameRate;
TTC = -dT / (1 - medDistRatio);
}
FP.5 : Performance Evaluation 1
This exercise is about conducting tests with the final project code, especially with regard to the Lidar part. Look for several examples where you have the impression that the Lidar-based TTC estimate is way off. Once you have found those, describe your observations and provide a sound argumentation why you think this happened.
TTC from Lidar is not correct because of some outliers and some unstable points from preceding vehicle’s front mirrors, those need to be filtered out . Here we adapt a bigger shrinkFactor = 0.1, to get more reliable and stable lidar points. Then get a more accurate results.
FP.6 : Performance Evaluation 2
This last exercise is about running the different detector / descriptor combinations and looking at the differences in TTC estimation. Find out which methods perform best and also include several examples where camera-based TTC estimation is way off. As with Lidar, describe your observations again and also look into potential reasons. This is the last task in the final project.
The task is complete once all detector / descriptor combinations implemented in previous chapters have been compared with regard to the TTC estimate on a frame-by-frame basis. To facilitate the comparison, a spreadsheet and graph should be used to represent the different TTCs.
when get a robust clusterKptMatchesWithROI can get a stable TTC from Camera. if the result get unstable, It’s probably the worse keypints matches.
the results in FP_6_Performance_Evaluation_2.csv
The TOP3 detector / descriptor combinations as the best choice for our purpose of detecting keypoints on vehicles are:
SHITOMASI/FREAK
AKAZE/BRISK
AKAZE/BRIEF