Beginner Tier Exercises
Test Your Understanding of Perception, Sensors, and Navigation Concepts
Overview
These exercises test your understanding of the concepts from the Beginner tier:
- B1: Introduction to Robotic Perception
- B2: Understanding Sensor Types
- B3: SLAM and Navigation Concepts
Complete these exercises before moving to the Intermediate tier.
Exercise 1: Perception Pipeline Ordering
Task: Put these perception pipeline stages in the correct order (1-4):
| Stage | Order (1-4) |
|---|---|
| Feature Extraction | ___ |
| Interpretation | ___ |
| Sensing | ___ |
| Preprocessing | ___ |
Bonus: For each stage, give one example of what happens there.
Click for answer
- Sensing - Camera captures pixels, LIDAR measures distances
- Preprocessing - Noise filtering, calibration, synchronization
- Feature Extraction - Edge detection, object detection, clustering
- Interpretation - "This is a person," "This area is navigable"
Exercise 2: Sensor Selection Challenge
Scenario: You're building a robot for each situation below. Choose the most appropriate sensor(s) and justify your choice.
Scenario A: Delivery Robot in Office Building
- Environment: Indoor, well-lit hallways
- Tasks: Navigate to rooms, avoid people and furniture
- Budget: Medium
Your answer:
- Primary sensor: _______________
- Secondary sensor: _______________
- Justification: _______________
Scenario B: Agricultural Drone for Crop Monitoring
- Environment: Outdoor, variable lighting
- Tasks: Fly over fields, detect plant health by color
- Budget: Low-Medium
Your answer:
- Primary sensor: _______________
- Justification: _______________
Scenario C: Robot Arm for Bin Picking
- Environment: Indoor factory
- Tasks: Identify and grasp objects of different colors/sizes
- Budget: Medium-High
Your answer:
- Primary sensor: _______________
- Secondary sensor: _______________
- Justification: _______________
Click for suggested answers
A: Delivery Robot
- Primary: 2D LIDAR - reliable obstacle detection, good for SLAM
- Secondary: RGB camera - for recognizing room signs/numbers
- Why: Indoor environment suits LIDAR well; camera adds recognition capability
B: Agricultural Drone
- Primary: RGB camera with multispectral option
- Why: Need color for plant health analysis; must work outdoors where depth cameras struggle; lightweight for flight
C: Bin Picking Robot
- Primary: RGBD camera (e.g., RealSense)
- Secondary: Force/torque sensor at gripper
- Why: Need both color (object identification) and depth (grasping location)
Exercise 3: SLAM Concept Questions
Answer these conceptual questions about SLAM:
Question 3.1
Why is SLAM called "the chicken-and-egg problem"?
Your answer: _______________
Question 3.2
A robot drives in a square and returns to start. Its odometry shows it's 0.5m away from where it started. What happened and how does SLAM handle this?
Your answer: _______________
Question 3.3
What does each color mean in a standard ROS 2 occupancy grid map?
- White: _______________
- Black: _______________
- Gray: _______________
Click for answers
3.1: SLAM is chicken-and-egg because:
- To build a map, you need to know where you are (to place observations correctly)
- To know where you are, you need a map (to match observations against)
- Neither can be solved first - they must be solved simultaneously
3.2: The 0.5m error is odometry drift from accumulated wheel slip and measurement errors. SLAM handles this through loop closure - when the robot recognizes it's back at the start, it adjusts the entire trajectory to eliminate the error.
3.3:
- White = Free space (robot can pass)
- Black = Occupied (wall/obstacle)
- Gray = Unknown (not yet observed)
Exercise 4: Navigation Components Identification
Task: Match each Nav2 component to its correct description.
| Component | Description |
|---|---|
| 1. Map Server | A. Generates velocity commands to follow path |
| 2. AMCL | B. Computes global path from start to goal |
| 3. Planner Server | C. Handles stuck situations (spin, backup) |
| 4. Controller Server | D. Loads and publishes the map |
| 5. Behavior Server | E. Estimates robot's pose on the map |
Answers: 1=, 2=, 3=, 4=, 5=___
Click for answers
1=D, 2=E, 3=B, 4=A, 5=C
Exercise 5: True or False
Mark each statement as True (T) or False (F):
| # | Statement | T/F |
|---|---|---|
| 1 | RGB cameras provide depth information directly | ___ |
| 2 | LIDAR works well in complete darkness | ___ |
| 3 | Time-of-Flight cameras are best for outdoor use | ___ |
| 4 | Global planning happens once, local planning happens continuously | ___ |
| 5 | SLAM can only work with LIDAR sensors | ___ |
| 6 | Occupancy grids store the probability of each cell being occupied | ___ |
| 7 | The perception pipeline always ends with sensor data | ___ |
Click for answers
- False - RGB cameras only capture light intensity (color), not depth
- True - LIDAR uses its own laser light source
- False - ToF cameras struggle outdoors due to infrared interference from sunlight
- True - Global planner finds the path once (or when replanning), local planner runs continuously to follow it
- False - Visual SLAM uses cameras, RGB-D SLAM uses depth cameras
- True - Each cell stores 0 (free) to 1 (occupied), with 0.5 meaning unknown
- False - Pipeline ends with interpretation/understanding, not raw sensor data
Exercise 6: Diagram Interpretation
Look at the perception pipeline diagram from B1 and answer:
- What enters the pipeline at the "Sensing" stage?
- What exits the pipeline at the "Interpretation" stage?
- If a camera image has sensor noise, which stage removes it?
- If the robot needs to classify "is this a person?", which stage does that?
Click for answers
- Raw sensor signals from the physical world (photons, laser pulses, forces)
- Semantic understanding for decision-making (e.g., "obstacle at 2m", "person detected")
- Preprocessing - noise filtering happens here
- Interpretation - semantic classification happens here
Exercise 7: Practical Scenario Analysis
Scenario: A mobile robot is navigating through a crowded hallway. It has a 2D LIDAR and an RGB camera.
Answer these questions:
7.1 Perception
What can each sensor contribute to understanding the scene?
- LIDAR: _______________
- Camera: _______________
7.2 Navigation Challenge
A person suddenly walks in front of the robot's planned path. Trace what happens:
- Which sensor detects the person first?
- Which navigation component reacts?
- What does the robot do?
7.3 Failure Mode
The hallway has a glass door. What problems might occur and why?
Click for answers
7.1:
- LIDAR: Distance to obstacles, wall positions, navigable space
- Camera: Person recognition, door signs, color-based identification
7.2:
- LIDAR detects the person as a new obstacle (faster update rate)
- Local planner (Controller Server) detects obstacle in trajectory
- Robot either: stops, slows down, or finds a local detour around the person
7.3: Glass is problematic because:
- LIDAR may pass through or give erratic readings (glass is semi-transparent to lasers)
- Robot might plan a path through the door if it appears as free space
- Solution: Use camera to detect visual features of the door, or add ultrasonic sensors
Self-Assessment Checklist
Before moving to the Intermediate tier, confirm you can:
- Explain the four stages of the perception pipeline
- Compare RGB cameras, depth cameras, and LIDAR
- Describe what SLAM does and why it's challenging
- Explain the difference between global and local planning
- Identify the main components of Nav2
- Understand occupancy grid maps
Next Steps
Congratulations on completing the Beginner tier exercises!
If you scored well on these exercises, you're ready for:
If you struggled with any topics, review the corresponding lesson before continuing.