Bathymetric Graph SLAM

Baseline SLAM framework for underwater vehicles. The algorithm gets a set of bathymetric submaps as input and corrects the global map constructed while refining the vehicle trajectory through a map-to-map registration followed by a pose graph optimization.

real_data_example

Paper introducing and applying the method

The method implemented is described in this paper and used in this one

@inproceedings{torroba2019towards,
  title={Towards Autonomous Industrial-Scale Bathymetric Surveying},
  author={Torroba, Ignacio and Bore, Nils and Folkesson, John},
  booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={6377--6382},
  year={2019},
  organization={IEEE}
}

@article{torroba2020pointnetkl,
  title={PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM},
  author={Torroba, Ignacio and Sprague, Christopher Iliffe and Bore, Nils and Folkesson, John},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={3},
  pages={4078--4085},
  year={2020},
  publisher={IEEE}
}

Dependencies (tested on Ubuntu 16.04 and 18.04)

AUVLIB here
PCL http://pointclouds.org/
G2O https://github.com/RainerKuemmerle/g2o
Ceres http://ceres-solver.org/

Note that for G2O to be used by this repo you need to install it at a system level. From the G2O build folder, run

sudo make install

Building

Clone this repository and create a build folder under the root, then execute

cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=../install ..
make -j4
make install

Finally, add the following line to your ~/.bashrc file adapted to your own installation

export PATH=$PATH:/path/to/folder/bathymetric_slam/install/share

Available apps

Under bin folder. The process outputs .png images with the maps of bathymetry and consistency error. The current script optimizes the graph with Ceres, but the app outputs a "graph.g2o" file which you can solve with G2O if preferred.

SLAM with simulated data

In order to test the framework with data from the SMARC simulator, use the toy dataset map_small under sim_data. You can visualize both the ground truth map and vehicle trajectory in the visualizer. To start the optimization process, hit "q".

./bathy_slam_real --simulation yes --bathy_survey ../sim_data/map_small/

The simulation outputs a measure of the error contained in the map, as well as the height maps and error plots as .png files. To increase the complexity of the sim dataset, increase the Gaussian noise to the vehicle's position estimate. In order to adapt the performance of the algorithm to the dataset, adjust the weights of the edges of the pose-graph accordingly and tune the GICP and the Ceres solver parameters. The algorithm is not by default tuned for the toy example map_small.

SLAM with real data

To run the SLAM solution with real data from a bathymetric survey, currently the input is in the form of a cereal file containing all the necessary information from your data files. You can find a real survey carried out with an ROV here. Download it, adjust the framework values, and test it.

./bathy_slam_real --simulation no --bathy_survey /path/to/datasets/mbes_pings.cereal

Generating your own cereal files from real surveys

Take a look at the AUVLIB toolbox in order to parse real MBES, SSS, navigation, etc data from the most common formats into .cereal files.

ignaciotb / bathymetric_slam

readme