Optical flow for indoor positioning in Drone Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Calculate displacement from optical flow data

Given the optical flow vectors detected by a drone's downward camera, what is the resulting displacement in meters after processing the flow data for 1 second?

Assume the drone's altitude is 2 meters, and the optical flow sensor reports pixel shifts that translate to velocity components as shown in the code.

Drone Programming

altitude = 2.0  # meters
flow_x = 0.1  # meters/second
flow_y = -0.05  # meters/second
time_seconds = 1

displacement_x = flow_x * time_seconds
displacement_y = flow_y * time_seconds

print(f"Displacement X: {displacement_x} m")
print(f"Displacement Y: {displacement_y} m")

A Displacement X: 0.0 m\nDisplacement Y: 0.0 m

B Displacement X: 2.0 m\nDisplacement Y: -1.0 m

C Displacement X: 0.2 m\nDisplacement Y: -0.1 m

D Displacement X: 0.1 m\nDisplacement Y: -0.05 m

💡 Hint

Multiply the velocity components by the time to get displacement.

Displacement is velocity multiplied by time. Here, flow_x and flow_y are velocities in meters per second, so multiplying by 1 second gives the displacement directly.

🧠 Conceptual

intermediate

Understanding optical flow limitations indoors

Which of the following is the main challenge when using optical flow sensors for indoor positioning of drones?

A Strong GPS signals interfering with optical sensors

B Lack of distinct visual features on uniform surfaces

C Excessive sunlight causing sensor overheating

D High altitude causing blurred images

💡 Hint

Think about what optical flow needs to detect movement.

Optical flow relies on detecting changes in visual features. Uniform surfaces indoors often lack distinct features, making it hard to track movement accurately.

🔧 Debug

advanced

Fix the error in optical flow velocity calculation

The following code attempts to calculate the velocity from optical flow pixel shifts and altitude, but it raises an error. Identify the cause and select the correct fixed code.

Drone Programming

pixel_shift_x = 15
pixel_shift_y = 10
altitude = 3.0
focal_length = 2.5
velocity_x = pixel_shift_x * altitude / focal_length
velocity_y = pixel_shift_y * altitude / focal_length

print(f"Velocity X: {velocity_x}")
print(f"Velocity Y: {velocity_y}")

A

velocity_x = pixel_shift_x * altitude / focal_length
velocity_y = pixel_shift_y * altitude / focal_length

B

velocity_x = pixel_shift_x * altitude // focal_length
velocity_y = pixel_shift_y * altitude // focal_length

C

velocity_x = pixel_shift_x / altitude / focal_length
velocity_y = pixel_shift_y / altitude / focal_length

D

velocity_x = pixel_shift_x * altitude * focal_length
velocity_y = pixel_shift_y * altitude * focal_length

💡 Hint

Check the operators used for division and multiplication.

The original code is correct and does not raise an error. Options A, C, and D change the formula incorrectly or use integer division which can cause wrong results.

📝 Syntax

advanced

Identify the syntax error in optical flow data processing

Which option contains a syntax error when trying to create a dictionary of pixel shifts mapped to their velocities?

Drone Programming

pixel_shifts = {'x': 12, 'y': 8}
altitude = 2.5
focal_length = 3.0
velocities = {axis: pixel_shifts[axis] * altitude / focal_length for axis in pixel_shifts}
print(velocities)

A velocities = {axis: pixel_shifts[axis] * altitude / focal_length for axis in pixel_shifts}

B velocities = {axis: pixel_shifts[axis] * altitude / focal_length for axis in pixel_shifts.keys()}

C velocities = {axis: pixel_shifts[axis] * altitude / focal_length for axis pixel_shifts}

D velocities = {axis: pixel_shifts[axis] * altitude / focal_length for axis in pixel_shifts.items()}

💡 Hint

Check the syntax of the for loop in the dictionary comprehension.

Option C is missing the 'in' keyword in the for loop of the dictionary comprehension, causing a syntax error.

🚀 Application

expert

Calculate total distance traveled using optical flow data

A drone collects optical flow velocity data every second for 5 seconds as follows (in meters/second): [0.1, 0.15, 0.12, 0.13, 0.20]. What is the total distance traveled in meters after 5 seconds?

A 0.70

B 0.75

C 0.65

D 0.60

💡 Hint

Sum all velocities and multiply by the time interval (1 second each).

Total distance = sum of velocities * time interval. Sum is 0.1+0.15+0.12+0.13+0.20 = 0.70 meters.

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