windowsub0406 / stereovision

separate road using v-disparity. test occupancy grid, stixels

Stereo Vision

Introduction

This project was coded in 2015 for self-driving project using stereo vision. I had conducted self-driving based on stereo vision. This code doesn't include detection & tracking. In this code, My point was preprocessing for exact obstacle detection.

Summary

• I used Block Matching(BM) Algorithm for a disparity map.
• I separated ground and unnecessary parts(above the height of a vehicle) by using v-disparity method.
• I coded occupancy grid which is one of the General Object Detection(GOD) methods.
• I tried stixels method.

Installation & Environment

Installation

• Dataset

  Daimler open dataset (TestData_c0_part1.tgz, TestData_c1_part1.tgz)

Environment

software

Windows 8.1(x64), Visual studio 2013, OpenCV 2.4.9

hardware

CPU : i3-4005U 1.72GHz, Memory : 6GB

Stereo matching Algorithm

I used Block Matching(BM) Algorithm in this project. but you can use (Semi-Global Matching)SGM Algorithm by changing alg = 0 to 1. As you can see, the SGM prints out better disparity map but I used a BM because of real-time processing. If your machine is good enough, I recommend SGM.

Separate ground (searching free space)

I separated ground using V-disparity. And I used RANSAC algorithm for line fitting. I had tried separating ground with geometric method previously, but v-disparity result is better.

For more details about V-disparity, check this paper (Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through ”V-disparity”Representation).

Except unnecessary parts (above the height of a vehicle)

I also erased unnecessary parts(above the height of a vehicle) in the same way as V-disparity.

Occupancy grid (top view)

This is a occupancy grid whis is one of the General Object Detection(GOD) methods.
Red color means ground, Yellow means obstacles within 13m, Blue means obstacles over 13m

Stixels

I also tried Stixels methods simply. This method is proposed from Daimler group. For more details, check this paper(The Stixel World - A Compact Medium Level Representation of the 3D-World).

Result

video result (Click for full video)

stereovision's Issues

could you briefly explain what mx1 mx2 my1 my2 yml files are?

Dataset used

Hi.. Could you please provide link to the dataset used?

incorrect max number of iterations

I think you have given the calculation of max number of iterations by the fomular:
int max_iteration = (int)(1 + log(1. - 0.99) / log(1. - pow(0.6, no_samples)));
which is incorrect. It should be log(1 - Psuccess)/log(1 - w^n).
for your implementation, the max number of iterations should be 10 not 5. Can the author correct me if I miss something?