HugoNip/UndirectDirectSLAM

Introduction

This project shows several methods for keypoint matching, including Single-Level ORB, Multi-Level ORB, Optical Flow based methods, and the keypoint matching result in direct method SLAM.

Requirements

OpenCV

Required Packages

OpenCV
OpenCV Contrib

Compile this Project

mkdir build
cd build
cmake ..
make 

Run

Keypoint Matching by using ORB features for stereo images

./build/ORB_KP_Stereo_opencv

Good Matches

Keypoint Matching by using ORB features for arbitary two frames

ComputeORB function is defined by OpenCV

./build/ORB_KP_opencv

Good Matches

Keypoint Matching by using ORB features for arbitary two frames

ComputeORB function is not defined by OpenCV

./build/ORB_KP_function

Good Matches

Keypoint Matching by using Optical Flow (Lucas–Kanade method, LK) for arbitary two frames

./build/LK_KP

Multi-Level Optical Flow

Keypoint Matching in Direct Method based SLAM

./build/directSLAM

Optical Flow

Keypoint Matching by using Multi-Level ORB features

./build/ORB_KP_Multilevel

Multi-Level result

Extract ORB cost = 0.0118106 seconds.
Match ORB cost = 0.0142241 seconds.
Matches: 112

Single-Level result

Extract ORB cost = 0.00274763 seconds.
Match ORB cost = 0.00102879 seconds.
Matches: 65

Partial Scale Invariant

ORB algorithm uses a multiscale image pyramid. An image pyramid is a multiscale representation of a single image, that consist of sequences of images all of which are versions of the image at different resolutions. Each level in the pyramid contains the downsampled version of the image than the previous level. Once orb has created a pyramid it uses the fast algorithm to detect keypoints in the image. By detecting keypoints at each level orb is effectively locating key points at a different scale. In this way, ORB is partial scale invariant.

SIFT

SURF

Reference

Source 1
Source 2
Source 3