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Reinforcement Learning Meets Visual Odometry

youtube_video

This is the code for the ECCV24 paper Reinforcement Learning Meets Visual Odometry (PDF) by Nico Messikommer*, Giovanni Cioffi*, Mathias Gehrig, and Davide Scaramuzza. For an overview of our method, check out our video.

If you use any of this code, please cite the following publication:

@Article{Messikommer24eccv,
  author  = {Nico Messikommer* and Giovanni Cioffi* and Mathias Gehrig and Davide Scaramuzza},
  title   = {Reinforcement Learning Meets Visual Odometry},
  journal = {European Conference on Computer Vision. (ECCV)},
  year    = {2024},
}

If you use the SVO library, please do not forget to cite the following publications:

@inproceedings{Forster2014ICRA,
  author = {Forster, Christian and Pizzoli, Matia and Scaramuzza, Davide},
  title = {{SVO}: Fast Semi-Direct Monocular Visual Odometry},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2014}
}
@ARTICLE{7782863,
  author={Forster, Christian and Zhang, Zichao and Gassner, Michael and Werlberger, Manuel and Scaramuzza, Davide},
  journal={IEEE Transactions on Robotics}, 
  title={SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems}, 
  year={2017},
  volume={33},
  number={2},
  pages={249-265},
  doi={10.1109/TRO.2016.2623335}}

Abstract

Visual Odometry (VO) is essential to downstream mobile robotics and augmented/virtual reality tasks. Despite recent advances, existing VO methods still rely on heuristic design choices that require several weeks of hyperparameter tuning by human experts, hindering generalizability and robustness. We address these challenges by reframing VO as a sequential decision-making task and applying Reinforcement Learning (RL) to adapt the VO process dynamically. Our approach introduces a neural network, operating as an agent within the VO pipeline, to make decisions such as keyframe and grid-size selection based on real-time conditions. Our method minimizes reliance on heuristic choices using a reward function based on pose error, runtime, and other metrics to guide the system. Our RL framework treats the VO system and the image sequence as an environment, with the agent receiving observations from keypoints, map statistics, and prior poses. Experimental results using classical VO methods and public benchmarks demonstrate improvements in accuracy and robustness, validating the generalizability of our RL-enhanced VO approach to different scenarios. We believe this paradigm shift advances VO technology by eliminating the need for time-intensive parameter tuning of heuristics.

Installation

Below are the instructions to run the RL framework with SVO.

RL Framework

Go to the directory of the repo (vo_rl/) and run the follwing command:

docker build -t vo_rl .

To launch the contain of the built image, first, the paths inside the launch_container.sh needs to be adjusted to point to the code, data and log directory. Once the paths are changed, the following command can be used:

bash launch_container.sh

The following commands can be used to install the SVO library:

cd svo-lib

mkdir build && cd build

cmake .. && make

SVO Library

In addition to the RL framework, there is also a separate dockerfile inside the svo-lib directory, which can be used to build the SVO library in a similar way as the RL framework.

Datasets

TartanAir

The TartanAir dataset can be downloaded here: TartanAir To speed up the dataloading, we convert the RGB images of the image_left directory to grayscale images.

The dataset should have the following structure:

TartanAir/
├── abandonedfactory/
│   ├── Easy/
│   │   ├── P000/
│   │   │   ├── image_left_gray/
│   │   │   │   ├── 000000_left.jpg
│   │   │   │   ├── ...
│   │   │   ├── pose_left.txt

EuRoC

The EuRoC dataset can be downloaded here: EuRoC

The dataset should have the following structure:

EuRoC/
├── MH_01_easy/
│   ├── mav0/
│   │   ├── cam0/
│   │   │   ├── data.csv
│   │   │   ├── data/
│   │   │   │   ├── 1403636579763555584.png
│   │   │   │   ├── ...
│   │   ├── state_groundtruth_estimate0/
│   │   │   ├── data.csv
│   │   │   ├── sensor.yaml

TUM-RGBD

The TUM-RGBD dataset can be downloaded here: TUM-RGBD

The dataset should have the following structure:

TUM-RGBD/
├── rgbd_dataset_freiburg1_desk/
│   ├── groundtruth.txt
│   ├── rgb.txt
│   ├── rgb/
│   │   ├── 1305031452.791720.png
│   │   ├── ...

Training

In a first step, set all the necessary training parameters, the path to the dataset, config and logging directory in the config.py file. To use wandb for logging, set the wandb_logging variable to True and specify the wandb tags and group for the specific training run in config.py file. The wandb initialization is done inside the ppo.py file, where the <entity> shoud be replaced before running. To train the model inside the docker container, run the following command by replacing <GPU_ID> with the GPU ID to be used:

CUDA_VISIBLE_DEVICES=<GPU_ID> python train.py

Testing

Before testing the model, set all the necessary testing parameters, the path to the dataset, config and logging directory in the config_eval.py file. To test the model inside the docker container, run the following command by replacing <GPU_ID> with the GPU ID to be used:

CUDA_VISIBLE_DEVICES=<GPU_ID> python play.py

To evaluate the performance of the different runs, run the /evaluation/evaluate_runs.py. First, add the paths pointing to the results of the play.py script to the corresponding method in the METHODS dict inside evaluate_runs.py. Additionally, also specify the OUT_DIR in the evaluate_runs.py file. Then, run the following command from the level of the vo_rl directory to evaluate the runs:

python -m evaluation.evaluate_runs