YOLO_ORB_SLAM3

This is an improved version of ORB-SLAM3 that adds an object detection module implemented with YOLOv5 to achieve SLAM in dynamic environments.

• Object Detection
• Dynamic SLAM

Fig 1 : Test with TUM dataset

Getting Started

0. Prerequisites

We have tested on:

OS = Ubuntu 20.04

OpenCV = 4.2

Eigen3 = 3.3.9

Pangolin = 0.5

ROS = Noetic

1. Install libtorch

Recommended way

You can download the compatible version of libtorch from Baidu Netdisk code: 8y4k, then

unzip libtorch.zip
mv libtorch/ PATH/YOLO_ORB_SLAM3/Thirdparty/

Or you can

wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.11.0%2Bcpu.zip
unzip libtorch-cxx11-abi-shared-with-deps-1.11.0%2Bcpu.zip
mv libtorch/ PATH/YOLO_ORB_SLAM3/Thirdparty/

2. Build

cd YOLO_ORB_SLAM3
chmod +x build.sh
./build.sh

Only the rgbd_tum target will be build.

3. Build ROS Examples

Add the path including Examples/ROS/YOLO_ORB_SLAM3 to the ROS_PACKAGE_PATH environment variable. Open .bashrc file:

gedit ~/.bashrc

and add at the end the following line. Replace PATH by the folder where you cloned YOLO_ORB_SLAM3:

export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/YOLO_ORB_SLAM3/Examples/ROS

Then build

chmod +x build_ros.sh
./build_ros.sh

Only the RGBD target has been improved.

The frequency of camera topic must be lower than 15 Hz.

You can run this command to change the frequency of topic which published by the camera driver.

roslaunch YOLO_ORB_SLAM3 camera_topic_remap.launch

4. Try

TUM Dataset

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE

ROS

roslaunch YOLO_ORB_SLAM3 camera_topic_remap.launch
rosrun YOLO_ORB_SLAM3 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE