If you use this code in an academic context, please cite the following RA-L 2022 paper.

G. Cioffi, T. Cieslewski, and D. Scaramuzza, "Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study," IEEE Robotics and Automation Letters (RA-L). 2022.

@InProceedings{CioffiRal2022
  author = {Cioffi, Giovanni and Ciesleski, Titus and Scaramuzza, Davide},
  title = {Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study},
  booktitle = {IEEE Robotics and Automation Letters (RA-L)},
  year = {2022}
}

These instructions have been tested on Ubuntu 18.04 and Ubuntu 20.04 and python 2.7 (python 3 support will come at a later point).

Prerequisites

Install Ceres Solver and COLMAP.

Build the repo

Git clone the repo:

git clone --recursive [email protected]:uzh-rpg/rpg_vision-based_slam.git

Build:

cd rpg_vision-based_slam

mkdir build

cd build

cmake .. -DCMAKE_BUILD_TYPE=Release

make -j4

We provide here instructions on how to run our SLAM solution using visual, inertial, and global positional measurements. Check below for working examples on the UZH FPV dataset and EuRoC dataset.

COLMAP

As first step, we run COLMAP to get an initial camera trajectory as well as a sparse 3D map.

We provide python scripts that can be used to generate config files for COLMAP (run COLMAP from command line). Check: scripts/python/create_colmap_project_$dataset-name$.py

Extract the camera trajectory from COLMAP output:

python scripts/python/extract_traj_estimate_from_colmap_$dataset-name$.py $FLAGS$

Continuous-time SLAM

Fit the B-spline to the camera trajectory:

(from the build folder)

./fit_spline_to_colmap $CONFIG_FILE$

Initial spatial aligment (scale and pose) of the B-spline to the global frame:

python scripts/python/initialize_spline_to_global_frame_spatial_alignment.py $FLAGS$

Align spline to the global frame:

(from the build folder)

./align_spline_to_global_frame $CONFIG_FILE$

Run full-batch optimization:

(from the build folder)

./optimize_continuous_time $CONFIG_FILE$

Discrete-time SLAM

Spatial aligment (scale and pose) of the camera trajectory estimated by COLMAP to the global frame:

python scripts/python/transform_colmap_to_global_frame.py $FLAGS$

Run full-batch optimization:

(from the build folder)

./optimize_discrete_time $CONFIG_FILE$

We give here an example on how to run the continuous-time SLAM formulation on the sequence indoor forward facing 3 snapdragon of the UZH FPV dataset.

Data preparation

Create the folder rpg_vision-based_slam/datasets/UZH-FPV/indoor_forward_3_snapdragon.

Extract the content of the .zip files raw data and leica measurements in this folder.

The file datasets/UZH-FPV/calib/indoor_forward_calib_snapdragon/camchain-imucam-..indoor_forward_calib_snapdragon_imu_simple.yaml contains a simplified version (for yaml parsing) of the calibration files.

Run COLMAP

Create the COLMAP project

python scripts/python/create_colmap_project_uzhfpv_dataset.py --env=i --cam=fw --nr=3 --sens=snap --cam_i=left

This script creates config files to use in COLMAP. It will also print in the terminal the commands to execute in order to run COLMAP:

colmap database_creator --database_path $path-to-root-folder$/datasets/UZH-FPV/colmap/indoor_forward_3_snapdragon/database.db

colmap feature_extractor --project_path $path-to-root-folder$/datasets/UZH-FPV/colmap/indoor_forward_3_snapdragon/feature_extractor_config.ini

colmap sequential_matcher --project_path $path-to-root-folder$/datasets/UZH-FPV/colmap/indoor_forward_3_snapdragon/sequential_matcher_config.ini

colmap mapper --project_path $path-to-root-folder$/datasets/UZH-FPV/colmap/indoor_forward_3_snapdragon/mapper_config.ini

Visualize results using the COLMAP gui:

colmap gui

File -> Import model -> Select folder containing the model, e.g. folder 0

colmap gui project_path --database_path $path-to-/database.db$ --image_path $path-to-img-folder$

Extract COLMAP estimated trajectory:

python scripts/python/extract_traj_estimate_from_colmap_uzhfpv.py --env=i --cam=fw --nr=3 --sens=snap --cam_i=left

Prepare Leica measurements:

python scripts/python/make_leica_minimal.py --env=i --cam=fw --nr=3 --sens=snap

Run Continuous-time SLAM

mkdir -p results/UZH_FPV

cd build

./fit_spline_to_colmap ../experiments/UZH_FPV/indoor_forward_3_snapdragon/colmap_fitted_spline/indoor_forward_3_snapdragon.yaml

python ../scripts/python/initialize_spline_to_global_frame_spatial_alignment_uzhfpv.py --config ../experiments/UZH_FPV/indoor_forward_3_snapdragon/spline_global_alignment/indoor_forward_3_snapdragon.yaml --env=i --cam=fw --nr=3 --sens=snap --gui

./align_spline_to_global_frame ../experiments/UZH_FPV/indoor_forward_3_snapdragon/spline_global_alignment/indoor_forward_3_snapdragon.yaml

./optimize_continuous_time ../experiments/UZH_FPV/indoor_forward_3_snapdragon/full_batch_optimization/continuous_time/indoor_forward_3_snapdragon.yaml

Run Discrete-time SLAM

cd build

python ../scripts/python/transform_colmap_to_global_frame.py --config ~/rpg_vision-based_slam/experiments/UZH_FPV/indoor_forward_3_snapdragon/colmap_global_alignment/indoor_forward_3_snapdragon.yaml --gui

./optimize_discrete_time ../experiments/UZH_FPV/indoor_forward_3_snapdragon/full_batch_optimization/discrete_time/indoor_forward_3_snapdragon.yaml

Plot results

Plot results of spline fitting:

python scripts/python/plot_results_spline_fitting_to_colmap_traj.py --config experiments/UZH_FPV/indoor_forward_3_snapdragon/colmap_fitted_spline/indoor_forward_3_snapdragon.yaml

Plot results of spline aligment:

python scripts/python/plot_results_spline_global_frame_alignment.py --config experiments/UZH_FPV/indoor_forward_3_snapdragon/spline_global_alignment/indoor_forward_3_snapdragon.yaml

Plot final results:

python scripts/python/plot_results_continuous_time.py --config experiments/UZH_FPV/indoor_forward_3_snapdragon/full_batch_optimization/continuous_time/indoor_forward_3_snapdragon.yaml

python scripts/python/plot_results_discrete_time.py --config experiments/UZH_FPV/indoor_forward_3_snapdragon/full_batch_optimization/discrete_time/indoor_forward_3_snapdragon.yaml

We give here an example on how to run the continuous-time and the discrete-time SLAM formulations on the sequence V2 01 easy of the EuRoC dataset.

Data preparation

Create the folder rpg_vision-based_slam/datasets/EuRoC/V2_01_easy.

Download the rosbag in this folder. Use the following script to extract the data:

python scripts/python/extract_from_euroc_rosbag.py --room=V2 --nr=1 --cam=right

The file datasets/EuRoC/calib/Vicon_room/calib.yaml contains the calibration file for this sequence.

Run COLMAP

Create the COLMAP project

python scripts/python/create_colmap_project_euroc_dataset.py --room=V2 --nr=1 --cam=right

Follow the output of the previous script to run COLMAP.

Extract COLMAP estimated trajectory:

python scripts/python/extract_traj_estimate_from_colmap_euroc.py --room=V2 --nr=1 --cam=right --colmap_model_id=0

Create global positional measurements from the ground truth:

python scripts/python/extract_euroc_groundtruth.py --room=V2 --nr=1

python scripts/python/make_global_measurements_euroc.py --room=V2 --nr=1 --freq=10.0 --noise=0.10

Run Continuous-time SLAM

mkdir -p results/EuRoC

cd build

./fit_spline_to_colmap ../experiments/EuRoC/V2_01_easy/colmap_fitted_spline/v2_01_easy.yaml

python ../scripts/python/initialize_spline_to_global_frame_spatial_alignment.py --config ~/rpg_vision-based_slam/experiments/EuRoC/V2_01_easy/spline_global_alignment/v2_01_easy.yaml --gui

./align_spline_to_global_frame ../experiments/EuRoC/V2_01_easy/spline_global_alignment/v2_01_easy.yaml

./optimize_continuous_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization/continuous_time/v2_01_easy.yaml

Run Discrete-time SLAM

python scripts/python/transform_colmap_to_global_frame.py --config ~/rpg_vision-based_slam/experiments/EuRoC/V2_01_easy/colmap_global_alignment/v2_01_easy.yaml --gui

./optimize_discrete_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization/discrete_time/v2_01_easy.yaml

We give here examples on how to run our SLAM algorithm with a sub-set of the sensor modalities.

We use the sequence V2 01 easy of the EuRoC dataset.

Global-Visual SLAM

cd build

For continuous time:

./optimize_gv_continuous_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization_gv/continuous_time/v2_01_easy.yaml

For discrete time:

./optimize_gv_discrete_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization_gv/discrete_time/v2_01_easy.yaml

Global-Inertial SLAM

For continuous time:

cd build

./fit_spline_to_gp_measurements ../experiments/EuRoC/V2_01_easy/fit_spline_on_gp_meas/v2_01_easy.yaml

./optimize_gi_continuous_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization_gi/continuous_time/v2_01_easy.yaml

For discrete time:

./optimize_gi_discrete_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization_gi/discrete_time/v2_01_easy.yaml

Visual-Inertial SLAM

The estimated trajectory by COLMAP needs to be aligned to a gravity aligned frame. This script is a good starting point to estimate gravity direction using accelerometer measurements.

For continuous time:

./optimize_vi_continuous_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization_vi/continuous_time/v2_01_easy.yaml

For discrete time

./optimize_vi_discrete_time ../experiments/EuRoC/V2_01_easy/full_batch_optimization_vi/discrete_time/v2_01_easy.yaml

Trajectory evaluation

Install the trajectory evaluation toolbox.

Single trajectory:

rosrun rpg_trajectory_evaluation analyze_trajectory_single.py $path-to-folder$

Multiple trajectories:

rosrun rpg_trajectory_evaluation analyze_trajectories.py $path-to-config$ --output_dir=$path$ --results_dir=$path$ --platform $value$ --odometry_error_per_dataset --plot_trajectories --rmse_table --rmse_boxplot

This repo uses some external open-source code:

• RPG trajectory evaluation toolbox
• RPG SVO Pro
• TUM Lie Group Cumulative B-splines
• COLMAP
• Kalibr
• UZH-FPV Open

Refer to each open-source code for the corresponding license.

If you note that we missed the information about the use of any other open-source code, please open an issue.