These relate to algorithms that have to do with robot’s processing and acting on the world around it. It is an unorganized compilation of our attempts to mimic the processes of the human brain. This does not include implementation specific details like tools and coding standards. For that see 00 - Roboting
Table of Contents
General Architecture of Robotics Systems
Perception
World Modeling
ICP Iterative Closest Point (ICP) Generalized Iterative Closest Point (GICP) Mahalanobis Distance
Pose Graphs Pose Graph Pose Graph Optimization
Special Groups Lie Algebra for EKF SE(3) SO(3)
State Estimation
This stuff was referencing http://asrl.utias.utoronto.ca/~tdb/bib/barfoot_ser17.pdf
What is State Estimation Applications of Deep Learning in State Estimation Measurement Fusion
3D Geometries 3D Numanclature Key Identities Poses and Transformations Rotation Representations Rotational Kinematics
Calibration Camera Calibration
Handling Non-Idealities Finding Covariance W Internal vs External Data Association RANSAC Robust Loss Functions Unbiased and Consistent
Linear-Gaussian Estimation Bayesian Inference Exploiting Sparsity in Batch Solution Kalman Filter LG Problem Statement Maximum A Posteriori Rauch-Tung-Striebel Smoother
Non-linear Non-Gaussian Estimation Bayes Filter Bayesian Inference Extended Kalman Filter Generalized Gaussian Filter Iterative Extended Kalman Filter Iterative Sigmapoint (Unscented) Kalman Filter Maximum A Posteriori Monte Carlo Method NLNG Problem Statement Particle Filter Sigmapoint (Unscented) Kalman Filter Sigmapoint (Unscented) Transformation Sliding-Window Filter Ways to Handle Non-linearities
Memory
(this one is harder to see, but what im trying to get here is that there is some sort of memory management here, something that I feel like exceeds world modeling)
Configuration
Action
End to End
memory worldModeling perception configuration action endToEnd