Vision Algorithms for Mobile Robotics (VAMR) Mini project

Report can be found here

Team Members

Hardik Shah ([email protected])
Deepak Ganesh ([email protected])
Aniruddha Sundararajan ([email protected])
Deepana Ishtaweera ([email protected])

Tested on

Screencasts were recorded on, ROG Zephyrus G15 GA503 GA503QM-HQ121R
OS: Ubuntu 22.04
CPU: 3.0 GHz AMD Ryzen 9 5900HS
RAM: 16 GB 3200MHz

Steps to setup

Setting up conda environment

Note: make sure you have miniconda3/ anaconda installed and working in the terminal
Note: first navigate into the folder

conda env create -f python_env/conda_config.yml

Optional: using pyenv virtual environment

pip3 install -r python_env/requirements.txt

Download the datasets

Use the following commands to download the benchmark datasets.

mkdir data && cd data
wget -O parking.zip https://rpg.ifi.uzh.ch/docs/teaching/2023/parking.zip
unzip parking.zip
wget -O kitti05.zip https://rpg.ifi.uzh.ch/docs/teaching/2023/kitti05.zip
unzip kitti05.zip
wget -O malaga.zip https://rpg.ifi.uzh.ch/docs/teaching/2023/malaga-urban-dataset-extract-07.zip
unzip malaga.zip
mv malaga-urban-dataset-extract-07 malaga
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=12IQMiJbkg5LW9epJfGxKL8U6VYO33fu7' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=12IQMiJbkg5LW9epJfGxKL8U6VYO33fu7" -O own.zip && rm -rf /tmp/cookies.txt
unzip own.zip

The data folder structure should be as follows.

├── data
│   ├── kitti
│   │   ├──05
│   │   │   ├── image_0
│   │   │   │   ├── ...
│   │   │   ├── image_1
│   │   │   │   ├── ...
│   │   │   ├── calib.txt
│   │   │   ├── times.txt
│   │   ├──poses
│   │   │   ├── ...
│   ├── malaga
│   │   ├── ...
│   ├── parking
│   │   ├──images
│   │   ├──K.txt
│   │   ├──poses.txt
│   ├── own
│   │   ├── ...

Run the app

Usage of the vo_pipeline.py file

usage: vo_pipeline.py [-h] [--dataset_dir DATASET_DIR] [--dataset_name DATASET_NAME] [--config CONFIG]

Visual Odometry Pipeline

optional arguments:
  -h, --help            show this help message and exit
  --dataset_dir DATASET_DIR
                        Path to the dataset directory
  --dataset_name DATASET_NAME
                        Name of the dataset: can be kitti, malaga, parking or own
  --config CONFIG       Path to the config file: can be config/params.yaml, config/params_kitti.yaml, config/params_malaga.yaml,
                        config/params_parking.yaml or config/params_own.yaml

Use the following python commands to run the vo pipeline for different datasets. Make sure to navigate to the folder root before running the commands.

python3 vo_pipeline.py --dataset_name kitti --config config/kitti.yaml
python3 vo_pipeline.py --dataset_name parking --config config/parking.yaml
python3 vo_pipeline.py --dataset_name malaga --config config/malaga.yaml
python3 vo_pipeline.py --dataset_name own --config config/own.yaml

To activate Bundle Adjustment, change the use_ba flag to True in the configs. To activate Bootstrapping when landmarks go below a threshold, change the use_bootstrap flag to True in the configs.

Note: Bootstrapping and Bundle Adjustment cannot be used together.

Note: the results are saved into a subfolder with the dataset name in the out/ folder

Screencasts of the Datasets

Here are the video demonstrations showcasing the monocular visual odometry pipeline. Our approaches have been evaluated using three widely recognized datasets: Parking, Kitti, Malaga, and an additional dataset that we generated ourselves. Visit the playlist on Youtube.