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pySLAM-D

pySLAM-D is a SLAM repository for RGB-D images in python that computes the camera trajectory and yileds a 3D reconstruction of the scene. This code is suitable for Reinforcement Learning purposes and utilizes existing C++ libraries for fast visual odometry, loop-closure detection, and pose graph optimization. We have tested this code for visual SLAM in the Habitat-Sim environement.

pySLAM-D habitat

Installation

We have tested this library in Ubuntu 18.04 and CentOS 7.

1. Creating the conda environment

We suggest using a conda environment.

conda update -q conda
conda create -q -n pyslamd python=3.7 opencv=3.4.* numpy boost py-boost cmake eigen
conda activate pyslamd

2. GTSAM

The gtsam library is used for pose graph optimization. Make sure to build it with python bindings.

3. Installing dependencies and third party libraries

We use Open3D for 3D data handling.

conda install -c open3d-admin open3d

We make use of the TEASER++ library for a robust and certifiable front-end. For loop-closure detection, we use the FBOW library and a modified version of pyfbow for python bindings. These two libraries are included in the Thirdpary folder. To install them, follow these instructions:

cd Thirdparty
chmod +x build.sh
./build.sh

4. Using with Habitat-Sim

run habitat.py

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SLAM code for RGB-D images in python.

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