This is our solution for the course project for NYU ROB-GY 6203 Robot Perception.

The above flowchart shows our pipeline to obtain Odometry for our robot.

1. Environment Setup
   Execute the following commands to set up the environment:

conda update conda
git clone https://github.com/ai4ce/vis_nav_player.git
cd vis_nav_player
conda env create -f environment.yaml
conda activate game

2. Installing LightGlue
   LightGlue can be installed by following the instructions in the official documentation, available here, or by executing these commands:

git clone https://github.com/cvg/LightGlue.git && cd LightGlue
python -m pip install -e .

3. Git clone repository
   Clone the vis_slam_game repository using:

https://github.com/Harshit0803/vis_slam_game.git

Replace the player.py with the modified player.py provided in this repository.

4. Play using the default keyboard player

python player.py

5. Exploring the Environment
   Navigate the environment using keyboard movement keys. The areas explored by the user will be visible on the map.

6. Completing the Exploration Phase
   Press the ESC key to complete the exploration phase.

7. Visual Place Recognition
   The query images will be processed using the Visual Place Recognition algorithm, specifically VLAD.

8. Identifying the Likely Location
   The location with the minimum covariance will be identified as the most probable location.

9. Auto-Navigation to Final Destination
   After pressing the ESC key, the AUTO_NAV feature in the code will guide the player to the final destination.

Note: The map displays probable locations with red dots based on the query image match. If the camera continually collides with walls, the auto-navigation feature can be toggled in player.py:

self.AUTO_NAV = True  #change this to enable/disable NAV

Project Video:

This project was created by Prof. Chen Feng (cfeng at nyu dot edu) of AI4CE Lab