Optical flow for indoor positioning in Drone Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize the optical flow sensor.

Drone Programming

optical_flow_sensor = OpticalFlowSensor([1])

Drag options to blanks, or click blank then click option'

Aport=3

Bport=0

Cport=2

Dport=1

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong port number causes the sensor not to initialize.

The optical flow sensor is connected to port 0 for indoor positioning.

2fill in blank

medium

Complete the code to read the optical flow data.

Drone Programming

flow_data = optical_flow_sensor.[1]()

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Aread

Bstart

Cstop

Dreset

Attempts:

3 left

💡 Hint

Common Mistakes

Using start() instead of read() does not return data.

The read() method retrieves the current optical flow data from the sensor.

3fill in blank

hard

Fix the error in the code to calculate displacement from flow data.

Drone Programming

displacement = flow_data.[1] * time_interval

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Aspeed

Bdistance

Cvelocity

Dposition

Attempts:

3 left

💡 Hint

Common Mistakes

Using distance attribute directly causes incorrect calculation.

The velocity attribute gives the flow velocity, which multiplied by time gives displacement.

4fill in blank

hard

Fill both blanks to update the drone's position using optical flow data.

Drone Programming

new_position = (current_position[0] + displacement[1], current_position[1] + displacement[2])

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A[0]

B[1]

C[2]

D[3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong indices causes position update errors.

Displacement is a tuple; index 0 is x-axis, index 1 is y-axis displacement.

5fill in blank

hard

Fill all three blanks to filter optical flow data for valid movement detection.

Drone Programming

filtered_flow = {k: v for k, v in flow_data.items() if v[1] [2] and v[3] 0}

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A>

B0

C<

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators or values causes incorrect filtering.

This filters values greater than -1 and less than 0 to detect valid negative movement.

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