📦project
┣ 📂dataset --> contains the Waymo Open Dataset sequences (empty to start)
┃
┣ 📂misc
┃ ┣ evaluation.py --> plot functions for tracking visualization and RMSE calculation
┃ ┣ helpers.py --> misc. helper functions, e.g. for loading / saving binary files
┃ ┣ objdet_tools.py --> object detection functions without student tasks
┃ ┗ params.py --> parameter file for the tracking part
┃
┣ 📂results --> binary files with pre-computed intermediate results (empty to start)
┃
┣ 📂student
┃ ┣ association.py --> data association logic for assigning measurements to tracks incl. student tasks
┃ ┣ filter.py --> extended Kalman filter implementation incl. student tasks
┃ ┣ measurements.py --> sensor and measurement classes for camera and lidar incl. student tasks
┃ ┣ objdet_detect.py --> model-based object detection incl. student tasks
┃ ┣ objdet_eval.py --> performance assessment for object detection incl. student tasks
┃ ┣ objdet_pcl.py --> point-cloud functions, e.g. for birds-eye view incl. student tasks
┃ ┗ trackmanagement.py --> track and track management classes incl. student tasks
┃
┣ 📂tools --> external tools
┃ ┣ 📂objdet_models --> models for object detection
┃ ┃ ┃
┃ ┃ ┣ 📂darknet
┃ ┃ ┃ ┣ 📂config
┃ ┃ ┃ ┣ 📂models --> darknet / yolo model class and tools
┃ ┃ ┃ ┣ 📂pretrained --> copy pre-trained model file here (empty to start)
┃ ┃ ┃ ┃ ┗ complex_yolov4_mse_loss.pth
┃ ┃ ┃ ┣ 📂utils --> various helper functions
┃ ┃ ┃
┃ ┃ ┗ 📂resnet
┃ ┃ ┃ ┣ 📂models --> fpn_resnet model class and tools
┃ ┃ ┃ ┣ 📂pretrained --> copy pre-trained model file here (empty to start)
┃ ┃ ┃ ┃ ┗ fpn_resnet_18_epoch_300.pth
┃ ┃ ┃ ┣ 📂utils --> various helper functions
┃ ┃ ┃
┃ ┗ 📂waymo_reader --> functions for light-weight loading of Waymo sequences
┃
┣ basic_loop.py
┣ loop_over_dataset.py

The following diagram contains an outline of the data flow and of the individual steps that make up the algorithm:

1. Clone this repo: git clone https://github.com/PoChang007/Lidar_Camera_Fusion.git
2. cd Lidar_Camera_Fusion

The following setup in the local machine can run the program successfully:

• Ubuntu 20.04
• Python 3.8.10
• PyTorch 1.9.1+cu111
• Open3D 0.13.0

All dependencies required for the project have been listed in the file requirements.txt.

The Waymo Open Dataset Reader is a very convenient toolbox that allows you to access sequences from the Waymo Open Dataset without the need of installing all of the heavy-weight dependencies that come along with the official toolbox. The installation instructions can be found in tools/waymo_reader/README.md.

This project makes use of three different sequences to illustrate the concepts of object detection and tracking. These are:

• Sequence 1 : training_segment-1005081002024129653_5313_150_5333_150_with_camera_labels.tfrecord
• Sequence 2 : training_segment-10072231702153043603_5725_000_5745_000_with_camera_labels.tfrecord
• Sequence 3 : training_segment-10963653239323173269_1924_000_1944_000_with_camera_labels.tfrecord

To download these files, you will have to register with Waymo Open Dataset first: Open Dataset – Waymo, if you have not already, making sure to note "Udacity" as your institution.

Once you have done so, please click here to access the Google Cloud Container that holds all the sequences. Once you have been cleared for access by Waymo (which might take up to 48 hours), you can download the individual sequences.

The sequences listed above can be found in the folder "training". Please download them and put the tfrecord-files into the dataset folder of this project.

The object detection methods used in this project use pre-trained models which have been provided by the original authors. They can be downloaded here (darknet) and here (fpn_resnet). Once downloaded, please copy the model files into the paths /tools/objdet_models/darknet/pretrained and /tools/objdet_models/fpn_resnet/pretrained respectively.

In the main file loop_over_dataset.py, you can choose which steps of the algorithm should be executed. If you want to call a specific function, you simply need to add the corresponding string literal to one of the following lists:

• exec_data : controls the execution of steps related to sensor data.

  • pcl_from_rangeimage transforms the Waymo Open Data range image into a 3D point-cloud
  • load_image returns the image of the front camera

• exec_detection : controls which steps of model-based 3D object detection are performed

  • bev_from_pcl transforms the point-cloud into a fixed-size birds-eye view perspective
  • detect_objects executes the actual detection and returns a set of objects (only vehicles)
  • validate_object_labels decides which ground-truth labels should be considered (e.g. based on difficulty or visibility)
  • measure_detection_performance contains methods to evaluate detection performance for a single frame

In case you do not include a specific step into the list, pre-computed binary files will be loaded instead. This enables you to run the algorithm and look at the results even without having implemented anything yet. The pre-computed results for the 3D object detection need to be loaded using this link. Please use the folder darknet first. Unzip the file within and put its content into the folder results.

• exec_tracking : controls the execution of the object tracking algorithm

• exec_visualization : controls the visualization of results

  • show_range_image displays two LiDAR range image channels (range and intensity)
  • show_labels_in_image projects ground-truth boxes into the front camera image
  • show_objects_and_labels_in_bev projects detected objects and label boxes into the birds-eye view
  • show_objects_in_bev_labels_in_camera displays a stacked view with labels inside the camera image on top and the birds-eye view with detected objects on the bottom
  • show_tracks displays the tracking results
  • show_detection_performance displays the performance evaluation based on all detected
  • make_tracking_movie renders an output movie of the object tracking results

Even without solving any of the tasks, the project code can be executed.

The object tracking part uses pre-computed lidar detections. Download the pre-computed lidar detections](https://drive.google.com/drive/folders/1IkqFGYTF6Fh_d8J3UjQOSNJ2V42UDZpO?usp=sharing) (~1 GB), unzip them and put them in the folder results.

Parts of this project are based on the following repositories:

• Simple Waymo Open Dataset Reader
• Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds
• Complex-YOLO: Real-time 3D Object Detection on Point Clouds

License