FPS-DS-SLAM

Author: Shubhendu Ranadive

FPS-DS-SLAM is a robust visual SLAM library that can identify and remove dynamic feature points for stereo camera configurations.

The system shows superior result in camera trajectory estimation of KITTI dataset compared to origin ORB-SLAM2.

1. License

FPS-DS-SLAM is built upon ORB-SLAM2 and under the same license accordingly.

ORB-SLAM2 is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md.

2. Prerequisites

The library is tested only in Ubuntu 18.04 and OpenCV 3.4.9.

C++11 or C++0x Compiler

We use the new thread and chrono functionalities of C++11.

Pangolin

We use Pangolin for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.

OpenCV

We use OpenCV to manipulate images and features, and to apply CNN model inference. Dowload and install instructions can be found at: http://opencv.org. Required at leat 3.4.5 with dnn module support.

Recommend install OpenCV from source with OpenCL turned on. This can dramatically speed up CNN inference with OpenCL GPU support.

Eigen3

Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.

DBoW2 and g2o (Included in Thirdparty folder)

We use modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.

git-lfs

We use git-lfs to track big fils in ModelsCNN/ directory.

3. Building FPS-DS-SLAM library and examples

Clone the repository:

git clone https://github.com/shubhendu-ranadive/FPS-DS-SLAM

We provide a script build.sh to build the Thirdparty libraries and ORB-SLAM2. Please make sure you have installed all required dependencies (see section 2). Execute:

cd FPS-DS-SLAM
chmod +x build.sh
./build.sh

This will create executables test, mask_rcnn, stereo_kitti and stereo_euroc in Examples folder.

4. Stereo Examples

KITTI Dataset

1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php

2. Execute the following command. Change KITTIX.yamlto KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to 00, 01, 02,.., 11.

./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER results/CamPoses.txt

EuRoC Dataset

1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

2. Execute the following first command for V1 and V2 sequences, or the second command for MH sequences. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.

./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/mav0/cam0/data PATH_TO_SEQUENCE/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt

./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data PATH_TO_SEQUENCE/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt

7. Processing your own sequences

You will need to create a settings file with the calibration of your camera. See the settings file provided for the KITTI datasets for stereo cameras. We use the calibration model of OpenCV. See the examples to learn how to create a program that makes use of the ORB-SLAM2 library and how to pass images to the SLAM system. Stereo input must be synchronized and rectified. RGB-D input must be synchronized and depth registered.

8. SLAM and Localization Modes

You can change between the SLAM and Localization mode using the GUI of the map viewer.

SLAM Mode

This is the default mode. The system runs in parallal three threads: Tracking, Local Mapping and Loop Closing. The system localizes the camera, builds new map and tries to close loops.

Localization Mode

This mode can be used when you have a good map of your working area. In this mode the Local Mapping and Loop Closing are deactivated. The system localizes the camera in the map (which is no longer updated), using relocalization if needed.

9. Saving Map Points

The map points are saved automatically to MapPointsSave.xyz file. This can be converted to pcd or ply using Point Cloud Library PCL

Acknowledgement

The entire project is build upon ORB-SLAM2