Skip to content

Time to collision estimation Using YoloV3 and Projecting Lidar point clouds to 2d image for vehicle tracking

Notifications You must be signed in to change notification settings

aditya-167/3D-Lidar-Camera_TTC

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

SFND 3D Object Tracking

Compute Time to collision using Yolo for object localization and detection and projecting the point cloud data to images.

  1. First, you will develop a way to match 3D objects over time by using keypoint correspondences.
  2. Second, you will compute the TTC based on Lidar measurements.
  3. 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.
  4. 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

Basic Build Instructions

  1. Clone this repo.
  2. Make a build directory in the top level project directory: mkdir build && cd build
  3. Compile: cmake .. && make
  4. Run it: ./3D_object_tracking.

About

Time to collision estimation Using YoloV3 and Projecting Lidar point clouds to 2d image for vehicle tracking

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published