Robotics Primer
The ideas behind robot perception, localization, and recognition, connected in a progression from intuition to mathematics and implementation.
Choose a path by its goals and prerequisites. Start at the beginning or jump to the topic you need.
Articles · 11Browser Labs · 7
Read the first step: Indoor robotics roadmap — from sensors to motion control →Background and reading guide No robot-building experience required; linked articles introduce frames and estimation mathematics.
- Articles · Learning guide · 2 min
Indoor robotics roadmap — from sensors to motion control
Connect sensing, localization, mapping, planning and control, then choose a reading path for the system you want to build.
Background and reading guide
No robot-building experience required; linked articles introduce frames and estimation mathematics.
- Articles · Fundamentals · 5 min
ROS 2 Primer — Safely Distributing Robot Control Across Nodes, Topics, and DDS
ROS 2 is a robotics middleware that splits sensing, perception, planning, and control into separate processes and connects them via DDS communication. This article covers nodes, topics, services, actions, QoS, TF2, lifecycle, implementation and debugging, along with the differences from ROS 1 and real-world operational cautions, with diagrams.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Browser Labs · Fundamentals · 3 min
Robot coordinate transforms: matrices, quaternions and TF
Use a numeric example to connect rotation, translation, inverse transforms, quaternions and ROS frames.
Background and reading guide
Vectors and matrix multiplication.
- Browser Labs · Hands-on · 5 min
Reproduce robot failures with rosbag2: inputs, TF and time
Build controlled replay comparisons and diagnose duplicate TF publishers and mixed time sources.
Background and reading guide
A browser for the Lab; ROS 2 topics, parameters and TF for optional Jazzy procedures.
- Articles · Fundamentals · 5 min
Dead Reckoning Primer — Integrating Wheels, IMU, and GNSS to Track a Robot's Motion
Dead reckoning is the foundational technique of updating a robot's pose and position by integrating wheel rotation, IMU readings, and velocity sensors. This article covers differential-drive kinematics, accumulating error, EKF-based correction, and implementations in indoor transport robots and autonomous driving, with equations and diagrams.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Articles · Fundamentals · 20 min
LiDAR-SLAM Primer — Knowing "Where Am I" from Laser Point Clouds
A camera infers distance indirectly from patterns of reflected light; a LiDAR measures distance directly from the time of flight of a laser pulse. LiDAR-SLAM is the technology built on registering the point clouds this active ranging sensor returns. This article works systematically from the two pillars of scan matching, ICP and NDT, through LOAM's feature design, Loop Closure and Graph SLAM, up to the lineage of landmark algorithms — LeGO-LOAM, Cartographer, LIO-SAM, FAST-LIO2.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Browser Labs · Hands-on · 3 min
Why ICP fails: initialization, outliers and symmetric geometry
Run 2D registration and distinguish residuals, correspondence coverage and true pose error.
Background and reading guide
Rigid transforms, nearest neighbors and NumPy arrays.
- Articles · Fundamentals · 15 min
Sensor Fusion Primer — Building One World Model From Many Sensors
A camera is strong on meaning, a LiDAR is strong on geometry, an IMU is strong on motion — and each has its own blind spot. From the probabilistic and optimization-based math that fuses them (Kalman Filters, Factor Graphs) to concrete pairings like VIO (Camera×IMU), LIO (LiDAR×IMU), and BEV Fusion (Camera×LiDAR), a systematic tour of how Sensor Fusion turns several sensors into a single world model.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Browser Labs · Fundamentals · 4 min
Why sensor fusion fails — timing, frames and extrinsic calibration
Diagnose fusion failures through timestamps, coordinate frames, mounting geometry and covariance.
Background and reading guide
IMUs, wheel odometry, ROS 2 topics and TF.
- Articles · Fundamentals · 16 min
VIO/LIO Primer — Combining an IMU with a Camera or LiDAR for Self-Localization
A camera alone, or a LiDAR alone, breaks down in certain situations. Combining either sensor with an IMU (accelerometer + gyroscope) covers for that weakness -- this is what VIO (Visual-Inertial Odometry) and LIO (LiDAR-Inertial Odometry) do. Starting from why an IMU alone cannot recover position, this article works through the two design philosophies -- Filter-based and Optimization-based -- and the landmark algorithms from ROVIO, OKVIS, VINS-Mono, and OpenVINS through LIO-SAM and FAST-LIO2.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Browser Labs · Hands-on · 3 min
How to evaluate SLAM — ATE, RPE, runtime and failures
Use synthetic trajectories to distinguish absolute and relative errors, then define matching, alignment and failure reporting for an evaluation.
Background and reading guide
Localization, coordinate transforms and root-mean-square error.
- Articles · Fundamentals · 13 min
Robot Arm Kinematics Primer — Forward Kinematics, Inverse Kinematics, and the Jacobian
Forward kinematics finds the hand's position and orientation from the joint angles; inverse kinematics solves the reverse problem; and the Jacobian bridges the two through "velocity." From building coordinate transforms with DH parameters, through analytic IK, numerical IK (Damped Least Squares), singularities, and redundant degrees of freedom — a ground-up, systematic account of the kinematics that governs how a robot arm moves.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Articles · Fundamentals · 14 min
Force Control and Impedance Control Primer — Building a Robot That Can Touch
Starting from what actually happens the instant a position-controlled robot arm touches a rigid object — and why that makes assembly, polishing, and human-contact tasks impossible — this article works through impedance control, which links force and displacement via a virtual spring, damper, and mass; admittance control, which reverses that causality; and hybrid position/force control, which separates position and force by spatial direction, building the ideas up from equations and diagrams.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Articles · Fundamentals · 8 min
Hydraulic Systems Primer — From Pascal's Principle to Force Control in Robots
Why can a small input move a hydraulic excavator's massive arm? This article treats the pump, control valves, cylinder, hydraulic fluid, sensors, and controller as a single closed loop, covering the pressure/flow/efficiency equations, current technology in construction and agricultural machinery, and electro-hydraulic conversion and current research, with diagrams.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Articles · Fundamentals · 5 min
ArduPilot Primer — State Estimation, Control, and Mission Design for an Autopilot
ArduPilot is an open-source autopilot that implements a vehicle's sensor fusion, attitude/position control, navigation missions, and failsafes. This article covers the differences between Copter, Plane, and Rover, plus EKF, PID, MAVLink, SITL, and real-vehicle safety testing, with diagrams.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.
- Articles · Fundamentals · 13 min
Swarm Control (Multi-Robot) Primer — Building Flocking, Formation, and Consensus
Swarm control solves problems a single robot can't, using nothing but "simple local rules" applied across many robots. From reproducing flocking with Boids, through consensus algorithms for agreement, to the three approaches to formation control — leader-follower, virtual structures, and behavior-based — this article systematically organizes the field around the divide between centralized and decentralized architectures.
Background and reading guide
Basic frames, velocity and sensor terminology; use the reading guide for background.