Implementing Visual SLAM - A Step-by-Step Guide

01 December 2022 - 5 mins read time
Tags: SLAM Computer Vision Robotics OpenCV Python

Implementing Visual SLAM: A Step-by-Step Guide with Code Snippets

Introduction

Visual Simultaneous Localization and Mapping (SLAM) is an essential task in autonomous robotics. It allows robots to build a map of an unknown environment while keeping track of their location in real-time. This tutorial walks through implementing a simple Visual SLAM system using Python and OpenCV.

Prerequisites:

We’ll go through three core steps:

  1. Feature Extraction
  2. Feature Matching
  3. Pose Estimation

1. Feature Extraction

The first step in Visual SLAM is to extract key features from the environment that the robot will use to localize itself. We’ll use OpenCV’s SIFT (Scale-Invariant Feature Transform) detector for feature extraction.

import cv2
import numpy as np

# Load the image
image = cv2.imread('map_image.jpg', 0)

# Initialize SIFT detector
sift = cv2.SIFT_create()

# Detect keypoints and descriptors
keypoints, descriptors = sift.detectAndCompute(image, None)

# Draw keypoints on the image
image_with_keypoints = cv2.drawKeypoints(image, keypoints, None)

# Display the image
cv2.imshow('SIFT Keypoints', image_with_keypoints)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code will output the image with keypoints detected using SIFT, which will be used for matching later.

2. Feature Matching

Once we have keypoints from two consecutive images (or frames from a video feed), we match them to track the robot’s movement.

# Assume we have two images: img1 and img2

# Detect features in both images
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# Use FLANN-based matcher to find the best matches
index_params = dict(algorithm=1, trees=5)
search_params = dict(checks=50)

flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)

# Apply ratio test to filter good matches
good_matches = []
for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good_matches.append(m)

# Draw matches
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None)

# Display the matches
cv2.imshow('Matches', img_matches)
cv2.waitKey(0)
cv2.destroyAllWindows()

The result of the above code is a side-by-side comparison of two frames with lines connecting matched features.

3. Pose Estimation

Now that we have matched features, we estimate the relative pose between the two frames. Using the Essential matrix, we can recover the camera’s position and orientation.

# Extract location of the matched keypoints
pts1 = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
pts2 = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)

# Estimate Essential Matrix
E, mask = cv2.findEssentialMat(pts1, pts2, focal=1.0, pp=(0, 0), method=cv2.RANSAC, prob=0.999, threshold=1.0)

# Recover pose from Essential Matrix
_, R, t, _ = cv2.recoverPose(E, pts1, pts2)

print(f"Rotation Matrix: {R}")
print(f"Translation Vector: {t}")

This outputs the rotation matrix (R) and translation vector (t), which describe the robot’s relative movement between two frames.

Conclusion

Visual SLAM is a complex but powerful technique that enables robots to navigate and understand their environments. In this post, we covered feature extraction, feature matching, and pose estimation using Python and OpenCV. This guide provides a simple introduction to the core concepts of Visual SLAM. You can extend this to handle more frames, loop closure, and map optimization for a full-fledged SLAM system.

Further Reading:

Example Images:

  1. SIFT keypoints detected on an image.
  2. Feature matching between two frames for tracking.
  3. Estimated pose outputs for the robot’s navigation.