How many questions are in this quiz?

This quiz has 120 questions on lidar, IMU, and encoder concepts used in robotics.

What quiz format is used?

Each question has 4 options and there is no timer, so you can answer at your own pace.

Is the difficulty beginner-friendly?

It’s mixed difficulty, starting with fundamentals and moving into more applied sensing and fusion ideas.

Can I choose the number of questions and difficulty?

Yes. You can select your question count and pick a difficulty level before starting.

What topics are covered besides definitions?

You’ll practice frames, units, drift and bias, wheel odometry errors, lidar artifacts, and sensor-fusion reasoning.

Robot sensing: lidar, IMU, encoders Quiz

About this quiz

What you’ll learn

Lidar, IMU, and encoder data are the backbone of localization and mapping, but each sensor comes with quirks that can break an otherwise solid pipeline. This quiz helps you connect the math to real robot behavior: what the sensor measures, what it doesn’t, and how errors show up.

Every question uses 4 options and there’s no timer, so you can slow down and reason through frames, units, and sign conventions. Choose how many questions you want to answer and set the difficulty to match your goal—quick review, steady practice, or a full mixed challenge.

Skills you’ll practice

Interpreting lidar scans (ranges, angles, point clouds) and common artifacts
IMU fundamentals: accelerometer vs gyroscope signals, bias, drift, and gravity
Encoder kinematics: ticks-to-distance, differential drive odometry, and slip effects
Coordinate frames and transforms (body/world frames, yaw-pitch-roll intuition)
Sensor fusion reasoning: when to trust which sensor and why

Common pitfalls and how this quiz balances difficulty

Many mistakes come from mixing units (deg vs rad), confusing frames (sensor vs base_link), or assuming sensors provide absolute truth (e.g., IMU yaw without a reference). You’ll also see traps around integrating acceleration, handling gyro bias, and over-trusting wheel odometry on low-friction surfaces.

Difficulty is balanced by mixing quick concept checks with applied scenarios: easier items focus on definitions and signal intuition, while harder ones target error models, integration drift, and fusion trade-offs. Mixed mode rotates topics so you don’t get stuck on just one sensor type for too long.