humorbei / low-cost-passive-camera-and-active-lidar-vision-for-obstacle-depth

Example of disparity map created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures).

Low cost Passive (camera) and Active (LIDAR) Vision for Obstacle Depth

Example of disparity map created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures).

Citation:

@article{chirella:2020,
  author = {Vito F. Chiarella, Thiago Rateke, Karla A. Justen, Antonio C Sobieranski, Sylvio L Mantelli, Eros Comunello, Aldo von Wangenheim},
  title = {Comparison between low-cost passive and active vision for obstacle depth},
  journal = {Revista de Ciência e Tecnologia (RCT)},
  volume = {6},
  year = {2020},
}

• Original Paper
• Step-by-step

Sensors used

• Two HP Webcam HD-4110
• One LIDAR Lite V2
• Two Micro Servo SG90 (to makes LIDAR rotations)

Comparisson

(a) Original Image, (b) Stereo Disparity Map (camera), (c) Point Cloud Map (LIDAR)

Must have OpenCV 3.1 or later installed with extra modules. Also Arduino IDE (https://www.arduino.cc/en/Guide/HomePage). And Processing (https://processing.org/) as well.

Stereo Disparity Map (from camera)

• To compile: g++ filteredDisparityMap.cpp -lopencv_core -lopencv_videoio -lopencv_highgui -lopencv_imgcodecs -lopencv_imgproc -lopencv_calib3d -lopencv_features2d -lopencv_ximgproc -o veRun

Point Cloud Map (from LIDAR)

• Control the servos with: lidarControl.ino
• Build the Point Cloud with: lidarPointCloud.pde