Optical flow for indoor positioning in Drone Programming - Mini Project: Build & Apply

Optical flow for indoor positioning

📖 Scenario: You are programming a small indoor drone to help it know how it moves inside a room. The drone uses a camera to see the floor and calculates how much it has moved by looking at the changes in the floor pattern. This is called optical flow.We will write a simple program that takes a list of movement vectors detected by the camera and calculates the drone's total movement inside the room.

🎯 Goal: Build a program that sums up the optical flow vectors to find the drone's total movement in the X and Y directions inside a room.

📋 What You'll Learn

Create a list of optical flow vectors with exact values

Add a variable to hold the total movement initialized to zero

Use a for loop to sum the X and Y movements separately

Print the total movement as a tuple (total_x, total_y)

💡 Why This Matters

🌍 Real World

Indoor drones use optical flow to understand how they move without GPS. This helps them fly safely inside buildings.

💼 Career

Understanding optical flow and vector summing is important for drone programmers and robotics engineers working on navigation systems.

1

Create the optical flow data

Create a list called optical_flow_vectors with these exact tuples representing movement: (1, 2), (-1, 3), (0, -2), (2, 1).

Drone Programming

# Create the list optical_flow_vectors with the given tuples
# Your code here

Hint

Use square brackets to create a list and put the tuples inside separated by commas.

2

Set up total movement variables

Create two variables called total_x and total_y and set both to 0 to hold the total movement in X and Y directions.

Drone Programming

optical_flow_vectors = [(1, 2), (-1, 3), (0, -2), (2, 1)]
# Create total_x and total_y variables set to 0
# Your code here

Hint

Use simple assignment to create variables and set them to zero.

3

Sum the optical flow vectors

Use a for loop with variables dx and dy to iterate over optical_flow_vectors. Inside the loop, add dx to total_x and dy to total_y.

Drone Programming

optical_flow_vectors = [(1, 2), (-1, 3), (0, -2), (2, 1)]
total_x = 0
total_y = 0
# Use a for loop to sum dx and dy into total_x and total_y
# Your code here

Hint

Use a for loop to get each tuple and add the parts to total_x and total_y.

4

Print the total movement

Write a print statement to display the total movement as a tuple: (total_x, total_y).

Drone Programming

optical_flow_vectors = [(1, 2), (-1, 3), (0, -2), (2, 1)]
total_x = 0
total_y = 0
for dx, dy in optical_flow_vectors:
    total_x += dx
    total_y += dy
# Print the total movement tuple
# Your code here

Hint

Use print() with parentheses around total_x and total_y to show the tuple.

Practice

1. What is the main purpose of using optical flow in indoor drone positioning?

easy

A. To track the drone's movement by analyzing camera images

B. To connect the drone to GPS satellites

C. To control the drone's battery usage

D. To measure the drone's altitude using sound waves

Solution

1. Step 1: Understand optical flow concept

   Optical flow uses camera images to detect movement by comparing changes between frames.

2. Step 2: Identify its use in indoor positioning

   Since GPS doesn't work well indoors, optical flow helps track position by visual movement detection.

3. Final Answer:

   To track the drone's movement by analyzing camera images -> Option A

4. Quick Check:

   Optical flow = movement tracking indoors [OK]

Hint: Optical flow tracks movement visually, not with GPS indoors [OK]

Common Mistakes:

• Confusing optical flow with GPS tracking
• Thinking optical flow measures altitude
• Assuming optical flow controls battery

2. Which of the following is the correct Python syntax to calculate optical flow using OpenCV's calcOpticalFlowPyrLK function?

easy

A. flow = cv2.calcOpticalFlowPyrLK(prev_img, next_img, prev_pts, None)

B. flow = cv2.calcOpticalFlowPyrLK(prev_pts, prev_img, next_img, None)

C. flow = cv2.calcOpticalFlowPyrLK(next_img, prev_img, None, prev_pts)

D. flow = cv2.calcOpticalFlowPyrLK(None, prev_img, prev_pts, next_img)

Solution

1. Step 1: Recall function parameter order

   The function calcOpticalFlowPyrLK expects parameters in order: previous image, next image, previous points, and next points (usually None to compute).

2. Step 2: Match parameters to options

   flow = cv2.calcOpticalFlowPyrLK(prev_img, next_img, prev_pts, None) matches this order correctly; others mix parameter positions incorrectly.

3. Final Answer:

   flow = cv2.calcOpticalFlowPyrLK(prev_img, next_img, prev_pts, None) -> Option A

4. Quick Check:

   Correct parameter order = flow = cv2.calcOpticalFlowPyrLK(prev_img, next_img, prev_pts, None) [OK]

Hint: Remember: prev_img, next_img, prev_pts, None order [OK]

Common Mistakes:

• Swapping image and points parameters
• Passing None in wrong position
• Confusing previous and next images

3. Given the following Python snippet for updating drone position using optical flow displacement:

dx, dy = 5, -3
position = [10, 10]
position[0] += dx
position[1] += dy
print(position)

What will be the output?

medium

A. [15, -3]

B. [5, 13]

C. [10, 10]

D. [15, 7]

Solution

1. Step 1: Understand position update

   Initial position is [10, 10]. Adding dx=5 to x gives 15. Adding dy=-3 to y gives 7.

2. Step 2: Calculate final position

   Updated position is [15, 7].

3. Final Answer:

   [15, 7] -> Option D

4. Quick Check:

   10+5=15 and 10-3=7 [OK]

Hint: Add dx to x and dy to y for new position [OK]

Common Mistakes:

• Mixing x and y updates
• Forgetting to add dy as negative
• Printing initial position instead of updated

4. Identify the error in this Python code snippet for calculating optical flow displacement:

prev_pts = np.array([[100, 150]], dtype=np.float32)
next_pts = np.array([[105, 145]], dtype=np.float32)
dx = next_pts[0][0] - prev_pts[0]
dy = next_pts[0][1] - prev_pts[1]

medium

A. Wrong data type for numpy arrays

B. Missing import statement for numpy

C. Incorrect indexing of prev_pts causing IndexError

D. Using subtraction instead of addition

Solution

1. Step 1: Check indexing of prev_pts

   prev_pts is a 2D array; prev_pts[0] is [100, 150], but prev_pts[1] does not exist (index error).

2. Step 2: Identify cause of error

   Accessing prev_pts[1] causes an IndexError; correct indexing should be prev_pts[0][1].

3. Final Answer:

   Incorrect indexing of prev_pts causing IndexError -> Option C

4. Quick Check:

   Indexing 2D arrays needs two indices [OK]

Hint: Use two indices for 2D arrays like prev_pts[0][1] [OK]

Common Mistakes:

• Using single index on 2D numpy arrays
• Confusing dx and dy calculations
• Ignoring numpy import errors

5. You want to improve indoor drone positioning by combining optical flow displacement with altitude data from a barometer. Which approach best integrates these two data sources in Python?

hard

A. Store altitude as a separate variable and never update position

B. Create a dictionary with keys 'x', 'y', 'altitude' updated each frame

C. Use optical flow data only and ignore altitude for simplicity

D. Replace optical flow with barometer readings entirely

Solution

1. Step 1: Understand data integration needs

   Combining horizontal movement (optical flow) and vertical position (altitude) requires a unified data structure.

2. Step 2: Choose appropriate data structure

   A dictionary with keys 'x', 'y', and 'altitude' allows updating all position components together each frame.

3. Final Answer:

   Create a dictionary with keys 'x', 'y', 'altitude' updated each frame -> Option B

4. Quick Check:

   Combine data in one structure for full 3D position [OK]

Hint: Use one dictionary to track x, y, and altitude together [OK]

Common Mistakes:

• Ignoring altitude data
• Keeping altitude separate without integration
• Replacing optical flow instead of combining