- 1. Andrew's Notes
- 2. Autonomy
- 2.1. Control
- 2.1.1. Controllers
- 2.2. Estimation
- 2.2.1. Applied Statistics for Stochastic Processes
- 2.2.1.1. Bayesian Inference
- 2.2.2. Filter-Based Estimation Algorithms
- 2.2.2.1. Filters Overview
- 2.2.2.2. The Kalman Filter (Time-Varying LQE)
- 2.2.2.3. The Kinematic Filters
- 2.2.2.4. The Luenberger Observer (LQE)
- 2.3. Math Fundamentals
- 2.3.1. 3D Geometry
- 2.3.1.1. Rotations Robotics Field Guide
- 2.3.2. Linear Algebra
- 2.3.2.1. Visualizing Matrices
- 2.4. Perception
- 2.4.1. Computer Vision
- 2.4.1.1. The Pinhole Camera Model
- 2.5. Search and Optimization
- 2.5.1. Least-Squares Optimization
- 2.5.2. Nonlinear Optimization
- 2.5.3. Optimization Over Lie Groups
- 2.6. Systems Implementation
- 2.6.1. Computer Science
- 2.6.1.1. Algorithms
- 2.6.1.2. Data Structures
- 2.6.2. Computer Simulations
- 2.6.2.1. Event-Based Collision Detection
- 2.6.2.2. The Inertial Measurement Unit (IMU) Sensor
- 2.6.2.3. The Rotor Blade Flapping Effect
- 2.6.3. Operating Systems
- 2.6.3.1. A Key Press
- 2.6.3.2. Multithreaded Executable
- 2.6.3.3. Networking Layers
- 2.6.4. Optimization Libraries
- 2.6.4.1. 2D Range-Bearing Landmark Resolution with Ceres
- 2.6.4.2. Ceres Solver Python Tutorial
- 2.7. Systems Theory
- 2.7.1. Mechanics
- 2.7.1.1. The Transport Theorem
- 2.7.2. Signals
- 2.7.2.1. Algorithms in Continuous vs Discrete Time
- 3. Philosophy
- 3.1. Aristotelian Science
- 3.2. Logical Fallacies as Bayesian Failures
- 3.3. Realism vs Nominalism
- 3.4. Thomas Aquinas on Reason and Revelation
- 4. Random
- 4.1. Money Balancing Math
- 4.2. Tenet Timelines
- 5. Recipes
- 5.1. Appetizers
- 5.2. Breakfast
- 5.3. Dessert
- 5.4. Dinner
- 5.5. Quick Stats
- 6. Software Runbooks
- 6.1. Avahi Runbook
- 6.2. Metrics Pipeline Debugging