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SlamPy

Python version Build Status Documentation Status

This project is a python wrapper to SLAM algorithms.

At the moment, we provide support for:

  • ORB_SLAM2
  • ORB_SLAM3

Example of usage with ORB_SLAM2

  • the jupyter notebook example_usage shows how to use ORB_SLAM2 with a sequence of the KITTI dataset
  • the jupyter notebook trajectory_example draw the camera trajectory and the point cloud

Change the settings

to change the algorithm settings you can modify the setting.yaml file in the line

SLAM.alg:'insert here your .py file that contains the wrapper class'

you can add at the file the other params that the method needs. to change from ORB_SLAM2 to ORB_SLAM3 you need to change only the SLAM.alg entry.

ORB_SLAM2/3 params

the params needed for this 2 algorithms are:

  • a vocabulary file
  • a setting file with the intrinsics parameters of the cameras and other configuration params

We provided the vocabulary file and the configuration file for ORB_SLAM2/3 for the KITTI_02 camera and the TUM freiburg3 camera in the slam_metohd/Settings folder, but you can add your owns using as model the files currently provided.

Docker

We provide a container on dockerhub, in which all the dipendences and the repository are already installed.

docker pull giordanolaminetti/slampy:tag

The tag correspond to the container you need:

  • base correspond to a container with installed only the dependences
  • orbslam2 correspond to a container with only ORB_SLAM2 installed
  • orbslam3 correspond to a container with only ORB_SLAM2 installed
  • latest correspond to a container with ORB_SLAM2 and ORB_SLAM3 installed

When the image is ready, you can create a new container running:

NAME="orb"
DATAPATH="/PATH/TO/KITTI/DATE/DRIVE_SYNC_FOLDER/"
TAG="latest"
sudo docker run -it \
                --name $NAME \
                --mount type=bind,source="$(pwd)",target=/slampy/slampy \
                -v $DATAPATH:"/slampy/slampy/Dataset":ro \
                -p 8888:8888 --rm\
                giordanolaminetti/slampy:$TAG /bin/bash

Doing so, the created container contains both the code and the Dataset (in read-only mode to prevent wrong behaviours)

You can have a test with the jupyter example running:

jupyter notebook --ip 0.0.0.0

Credits:

Our project has been developed starting from other repositories, in particular: