Drivable Ground Path Detection Using Semantic Segmentation

This repository holds the implementation of detecting the drivable path in the ground plane for a mobile robot in an indoor environment. A U-Net based Semantic Segmentation model is trained on a custom dataset collected on the campus of the University of Maryland, College Park. This project is done as part of the course 'ENPM673 - Perception for Autonomous Robots' at the University of Maryland, College Park.

Team Members:

• Adarsh Malapaka* (amalapak@terpmail.umd.edu)
• Kumara Ritvik Oruganti* (okritvik@terpmail.umd.edu)
• Sparsh Bhogavilli* (sbhogavi@terpmail.umd.edu)
• Sai Sandeep Adapa (sadapa@umd.edu)
• Venkata Sai Ram Polina (sairamp@umd.edu)

Note:

• * Denotes equal contribution.
• Please contact any one of the team members (listed above) to gain access to the dataset, if you're interested!

Pipeline:

Training:

• Check out the usage.md for details on training & testing the model on a machine with a CUDA enabled GPU.

Training Curves

Train Loss v/s Epoch	Train IOU v/s Epoch

Validation Loss v/s Epoch	Validation IOU v/s Epoch

Testing

Test results on Laptop

• The model is 3-fold Cross-Validated & the following are the results.

3-Fold Cross-Validated Test IOU

Metric	Fold-1	Fold-2	Fold-3	Average
Test IOU	0.998	0.998	0.999	0.9983

Visualization

• The following image shows the segmentation output on an image from the test set.

It can be observed that the pillar is classified as Non-drivable region.

Deploying on Mobile Robot

• A camera has been mounted on a mobile robot platform and a Raspberry Pi, which is integrated into the platform, is utilized to transmit the camera's video stream to a laptop computer.
• Inference is then conducted on the video stream on the laptop using the inference_laptop.py script.
• The below two images are samples of the same.

Detected Ground (Feet labeled as non-drivable)	Detected Ground (Floor-mat labeled as drivable)

Demo Videos

• 3rd-person Point-of-View of Mobile Robot transmitting video stream from a corridor & inference on laptop (in top-left)

• Laptop screen capture of inference being run on video stream received from the robot.

Testing on Jetson TX2

• The learned PyTorch model was converted to ONNX format on the laptop & then to TensorRT engine on a Jetson TX2 platform.
• The following is the terminal output of successful TensorRT conversion.

Dataset:

• The dataset used for this project is made from the videos collected from the following locations on the University of Maryland's College Park campus.

  

Drop-down menu: Key for Depicted Locations (with number of videos recorded)

1. Atlantic Bldg (1 video)

2. J.M. Patterson Bldg (2 videos)

3. Chem/Nuclear Bldg (1 video)

4. A.V. Williams Bldg (1 video)

5. Brendan Iribe Center (1 video)

6. Glenn Martin Hall (2 videos)

7. Plant Sciences Bldg (1 video)

8. Psychology/Biology Bldg (1 video)

9. Symons Hall (1 video)

10. Woods Hall (1 video)

11. Tydings Hall (1 video)

12. Lefrak Hall (1 video)

 

Collection Methodology

Robot used for Data Collection	Data Collection in Glenn Martin Hall

Video Specifications

• Resolution: 1280 x 720 (16:9 ratio)
• Video Duration: 5 minutes
• Framerate: 30fps
• Height of Camera: 12 cm approx (from the ground)

Image extraction

• Every 100th frame of each video was extracted to form a total of 1260 images in the dataset.

Data Annotation

• The tool Dataloop is used for annotating the ground truth masks on the images.

Data Augmentation

• Data Augmentation techniques like Random crop, Horizontal flip & brightness changes are used.