#Robotics Primer

Robotics Primer September 7, 2026

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.

Fundamentals · 20 min Robotics Primer September 7, 2026

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.

Fundamentals · 16 min Computer Vision Primer September 7, 2026

Visual-SLAM Primer — How a Camera Learns "Where Am I"

With no GPS and no map, a robot can still find its own position and build a map of its surroundings from camera images alone. From feature tracking through epipolar geometry, the difference between Visual Odometry and SLAM, the lineage of landmark algorithms from ORB-SLAM to LSD-SLAM and DSO, and Bundle Adjustment, a systematic ground-up tour of how Visual-SLAM actually works.

Fundamentals · 18 min Robotics Primer September 6, 2026

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.

Fundamentals · 15 min Machine Learning Primer September 5, 2026

Object Detection & Semantic Segmentation Primer — Reading "What Is Where" From an Image

Object Detection draws boxes around things; Semantic Segmentation colors in every pixel by meaning. From the difference between the two, through Instance and Panoptic Segmentation, the lineage from R-CNN to YOLO, DETR and DINO, all the way to their 3D point-cloud counterparts — a systematic tour of the fundamentals of scene understanding.

Fundamentals · 14 min Robotics Primer September 4, 2026

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.

Fundamentals · 13 min Robotics Primer September 4, 2026

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.

Fundamentals · 14 min Robotics Primer September 4, 2026

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.

Fundamentals · 13 min Robotics Primer September 3, 2026

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.

Fundamentals · 5 min