# YOLO-DynaSLAM

**Repository Path**: byrds/YOLO-DynaSLAM

## Basic Information

- **Project Name**: YOLO-DynaSLAM
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-10
- **Last Updated**: 2024-12-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# YOLO Dynamic ORB_SLAM

YOLO Dynamic ORB_SLAM is a visual SLAM system that is robust in dynamic scenarios for RGB-D configuration.
See our other repository for related work: https://github.com/bijustin/Fast-Dynamic-ORB-SLAM/
Our paper is located here: https://github.com/bijustin/YOLO-DynaSLAM/blob/master/dynamic-orb-slam.pdf

We provide one example to run the SLAM system in the [TUM dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) as RGB-D.

Example result (left are without dynamic object detection or masks, right are with YOLOv3 and masks), run on [rgbd_dataset_freiburg3_walking_xyz](https://vision.in.tum.de/data/datasets/rgbd-dataset/download):

<img src="https://github.com/bijustin/Flow-DynaSLAM/blob/master/imgs/Dyna_NOyolo.png" width="320" height="240"> <img src="https://github.com/bijustin/Flow-DynaSLAM/blob/master/imgs/Dyna_yolo.png" width="320" height="240">

<img src="https://github.com/bijustin/Flow-DynaSLAM/blob/master/imgs/SLAM_NOyolo.png" width="320" height="240"> <img src="https://github.com/bijustin/Flow-DynaSLAM/blob/master/imgs/SLAM_yolo.png" width="320" height="240">

## Getting Started
- Install ORB-SLAM2 prerequisites: C++11 or C++0x Compiler, Pangolin, OpenCV and Eigen3  (https://github.com/raulmur/ORB_SLAM2).
- Install boost libraries with the command `sudo apt-get install libboost-all-dev`.
- Install python 2.7, keras and tensorflow, and download the `yolov3.weights` model from this address: https://pjreddie.com/media/files/yolov3.weights. 
- Clone this repo:
```bash
git clone https://github.com/bijustin/YOLO-DynaSLAM.git
cd YOLO-DynaSLAM
```
```
cd YOLO-DynaSLAM
chmod +x build.sh
./build.sh
```
- Place the `yolov3.weights` model in the folder `YOLO-DynaSLAM/src/yolo/`.

## RGB-D Example on TUM Dataset
- Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.

- Associate RGB images and depth images executing the python script [associate.py](http://vision.in.tum.de/data/datasets/rgbd-dataset/tools):

  ```
  python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
  ```
These associations files are given in the folder `./Examples/RGB-D/associations/` for the TUM dynamic sequences.

- Execute the following command. Change `TUMX.yaml` to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change `PATH_TO_SEQUENCE_FOLDER` to the uncompressed sequence folder. Change `ASSOCIATIONS_FILE` to the path to the corresponding associations file. `YOLO`is an optional parameter.

  ```
  ./Examples/RGB-D/rgbd_tum_yolo Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE (YOLO)
  ```
  
If `YOLO` is **not** provided, only the geometrical approach is used to detect dynamic objects. 

If `YOLO` is provided, Yolov3 is used to segment the potential dynamic content of every frame. 

## Acknowledgements
Our code builds on [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2) and [DynaSLAM](https://github.com/BertaBescos/DynaSLAM).

# YOLO Dynamic ORB_SLAM