The object-detector-fusion is used for detecting and tracking objects from data that is provided by a 2D LiDAR/Laser Scanner and a depth camera. The detection working principle is largely based on obstacle_detector created by Mateusz Przybyla, which used a density-based clustering method to group point clouds and create a geometric representation of objects within the sensor vicinity. The objects are detected by camera first, and then the measurement is corrected by LiDAR detection result, hence the "fusion" name. The dynamic state of the detected objects (position, speed, acceleration) is estimated (tracked) using particle filter.

This package is designed to work with following sensors:

1. RPLiDAR A1 Laser Scanner
2. Intel RealSense D435 Depth Camera

that are equipped on a land-based autonomous system.

1. ROS Melodic Morenia
2. rplidar
3. realsense-ros
4. depthimage-to-laserscan
5. obstacle_detector
6. jsk_visualization

1. Clone and add to your catkin package and run catkin_make

Run the following commands on the terminal. To start the fusion nodes:

roslaunch object-detector-fusion fusion.launch

Start the nodes with only camera:

roslaunch object-detector-fusion camera.launch

Start the nodes with only lidar (no tracking available):

roslaunch object-detector-fusion lidar.launch

You should see the output on the rviz main screen.

To obtain 2D depth data from 3D depth data, this package use depthimage-to-laserscan which subscribes sensor_msgs/Image type message in camera/depth/image_rect_raw and publishes sensor_msgs/LaserScan message, the 2D depth data that has been converted to laserscan-type message. camera_obstacle_extractor node converts this message into set of objects which are publishes as custom type obstacles_detector/Obstacles. lidar_obstacle_extractor does the same to the sensor_msgs/LaserScan published by rplidarNode. Green circle with red concentric circle represents detected objects from camera data. Beige and blue circle represents detected objects from lidar data.

particle_filter node will use the measurement data from these detected objects results to estimate dynamic state of an object as well as reduce measurement noise. A magenta box represents mean of the particles used to approximate the state and black dots surrounding the box represents the aforementioned particles.

)