Keypoints detection and matching between binary masks

I am trying to match keypoints using opencv (tutorial) between images shown below. The thing is that I am not sure if I need to adjust some parameters or I am entirely using wrong method. Taking only right side of map.png did not help either. Here is my code and also result.

import numpy as np
import cv2
import matplotlib.pyplot as plt

img1 = cv2.imread('../map.png',0)
img2 = cv2.imread('../mask.png',0)

orb = cv2.ORB_create()

kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)

bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

matches = bf.match(des1,des2)

matches = sorted(matches, key = lambda x:x.distance)

img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:20], None, flags=2)
cv2.imwrite('test.png', img3)

Feature detectors such as ORB used by you are designed to match feature points between images that differ in translation, rotation and scale. They are not intended to be used when images differ significantly in perspective (that is your case) and therefore your approach doesn't work. Moreover, such algorithms are designed for images that are rich in texture such as photos. In your case the features are repetative (multiple feature points extracted from first image, such as line endings, can be matched to a single point in the other). In your case you should consider another features such as those based on lines intersections, see this tutorial for more information. This is only a hint, not the solution for your problem as it is really challenging.