Compute Camera-based TTC

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.

The task is complete once the code performs as described and adds the keypoint correspondences to the "kptMatches" property of the respective bounding boxes. Also, outlier matches have been removed based on the euclidean distance between them in relation to all the matches in the bounding box.

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.

The task is complete once the code is functional and returns the specified output. Also, the code must be able to deal with outlier correspondences in a statistically robust way to avoid severe estimation errors.