#Sensor Fusion

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 Connected & Autonomous Driving September 3, 2026

Occupancy Grid Mapping — Turning LiDAR and Camera Evidence into Traversability

An occupancy grid divides space into cells and represents occupied, free, and unknown space probabilistically. Learn Bayesian and log-odds updates, inverse sensor models, ray casting, dynamic decay, 3D voxels, SLAM, costmaps, and planning.

Fundamentals · 8 min Control Engineering Primer September 3, 2026

Control Engineering Primer: State Observers and the Linear Kalman Filter

Sensor readings are not the state itself. This primer explains observability, Kalman predict/update equations, covariance, numerical examples, control integration, robot implementation, and fault-safe behavior.

Fundamentals · 5 min Connected & Autonomous Driving September 3, 2026

Occupancy Grid Mapping — Turning LiDAR and Camera Evidence into Traversability

An occupancy grid divides space into cells and represents occupied, free, and unknown space probabilistically. Learn Bayesian and log-odds updates, inverse sensor models, ray casting, dynamic decay, 3D voxels, SLAM, costmaps, and planning.

Fundamentals · 8 min