Module 02 · Phase 2: Hardware engineering · Flagship
Sensor Selection, Trade-offs & Integration
Choose, place, wire, drive, calibrate and validate sensors. Each sensor is covered by one fixed template, from what it measures to how it fails, with product facts taken only from manufacturer datasheets.
This overview is open. 22 of 22 guides are written; the others show Coming soon until they are complete and tested.
Questions this module answers
- Which sensor combination fits this robot, environment, budget and compute?
- What is the difference between accuracy and precision, resolution and accuracy, measurement rate and end-to-end latency, datasheet and real-world performance?
- Which datasheet specifications must I check before buying?
- How do I mount, interface, synchronize and validate each sensor, and how does it fail?
What it covers
- Monocular, stereo, RGB-D, event and thermal cameras; global vs rolling shutter
- 2D/3D, mechanical, solid-state, flash and FMCW LiDAR
- IMU, GNSS, RTK, GNSS/INS, barometer, magnetometer
- Radar, ultrasonic, wheel encoders, depth, force/torque, tactile, UWB, underwater sonar
- Selection criteria: range, FOV, accuracy, precision, resolution, rates, latency, noise, robustness, power, compute, interface, calibration, reliability, cost
Guides and design decisions
A. Engineering Guides (8)
- Sensor combination: camera + IMU
- Sensor combination: LiDAR + IMU
- Sensor combination: camera + LiDAR
- Sensor combination: GNSS + IMU
- Sensor combination: encoder + IMU
- Sensor combination: GNSS + IMU + LiDAR
- Sensor combination: camera + LiDAR + radar
- Mounting, interfaces, ROS 2 drivers and validation checklist
B. Design & Selection (14)
- Sensor selection criteria: reading a datasheet
- Accuracy vs precision, resolution vs accuracy, rate vs latency
- Cameras: monocular, stereo, RGB-D, event, thermal, shutter type
- LiDAR: 2D, 3D, spinning, solid-state, flash, FMCW
- Inertial and positioning: IMU, GNSS, RTK, GNSS/INS, barometer, magnetometer
- Radar, ultrasonic, encoders, force/torque, tactile, UWB and sonar
- Sensor selection: indoor mobile robots
- Sensor selection: outdoor ground robots
- Sensor selection: UAVs
- Sensor selection: humanoid robots
- Sensor selection: robotic manipulators
- Sensor selection: warehouse robots
- Sensor selection: agricultural robots
- Sensor selection: underwater robots
Diagnostics, tools and challenges
C. Troubleshooting (6)
- Sensor data drops intermittentlyA topic stalls or loses messages at irregular intervals.
- IMU orientation driftsRoll, pitch or yaw creeps while the robot is stationary.
- GPS position jumpsThe reported position leaps by metres with no robot motion.
- Barometer altitude fluctuatesAltitude oscillates or steps with no change in height.
- Camera images contain motion blurFeature tracking degrades when the robot moves.
- LiDAR point cloud is distortedWalls look curved or doubled while the robot moves or turns.
D. Interactive tools (2)
- Sensor Selection CalculatorApplication, environment, range, accuracy, rate, budget, compute and power in; candidate sensors and combinations, trade-offs, integration challenges and datasheet items to verify out. Preliminary guidance.
- Sensor Comparison ToolCompare camera, LiDAR, IMU, GNSS, radar, barometer and other sensors side by side.
E. Design challenges (1)
- Challenge 1: Design an Autonomous Mobile RobotIndoor differential-drive robot: sensors, motor sizing, compute, power, ROS 2, LiDAR+IMU, SLAM, Nav2, tuning and validation.
Practical deliverables
- Sensor selection calculator
- Sensor comparison tables
- Sensor selection checklists
- Real-world sensor architecture examples (cited from datasheets, with dates)
Read first: existing FixTheRobot lessons and labs
This module builds on these instead of repeating them.
- Complementary Strengths and Weaknesses of Cameras and LiDAR Perception · Lesson
- Depth Cameras and RGB-D Data Perception · Lesson
- LiDAR Point Clouds for SLAM SLAM · Lesson