Installation and Requirement

First add vcpkg as submodule to your project:

git submodule add https://github.com/microsoft/vcpkg

Then run the following script on Windows:

.\vcpkg\bootstrap-vcpkg.bat

on the bash:

./vcpkg/bootstrap-vcpkg.sh

The bootstrap script performs prerequisite checks and downloads the vcpkg executable.

set the path:

export VCPKG_ROOT=$PWD/vcpkg
export PATH=$VCPKG_ROOT:$PATH

Setting VCPKG_ROOT tells vcpkg where your vcpkg instance is located.

Now you can run:

cmake -S . -B build   -DCMAKE_TOOLCHAIN_FILE=./vcpkg/scripts/buildsystems/vcpkg.cmake

cmake --build build --parallel

To build the rerun, just comment everything in the CMakeLists.txt and only leave this part:

include(FetchContent)
FetchContent_Declare(rerun_sdk URL https://github.com/rerun-io/rerun/releases/latest/download/rerun_cpp_sdk.zip)
FetchContent_MakeAvailable(rerun_sdk)

The reason is building arrow_cpp might take very long time. Also install the rerun server via:

Install the python packages:

pip3 install rerun-sdk==0.20.3
conda install -c conda-forge opencv
pip install graphslam
conda install conda-forge::gtsam
conda install conda-forge::matplotlib
conda install conda-forge::plotly

Also create this soft link.

ln -s /home/$USER/workspace/robotic_notes/docs/ /home/$USER/anaconda3/envs/robotic_notes/docs

Lie Group and Lie Algebra

• Lie Group and Lie Algebra
• manif
  • SE2 localization
  • SE2 SAM
• Sophus

Topology and Configuration of Robot and Space

• Configuration of Robot
• Configuration Space - (C-space )
• Degrees of freedom.
• Task Space
• Work Space
• Dexterous space:
• dof
• Topology
  • Different way to represent C-space
    • Explicit
    • Implicit
• Algebraic topology (playlist)
• Non-Holonomic Constraints, Pfaffian Constraints and Holonomic Constraints

Aerial Robotics

• Aerial Robotics

PX4

• PX4

Differential Drive Robots and Wheel odometry

• Kinematics of Differential Drive Robots and Wheel odometry
• Velocity-based (dead reckoning)
  • Forward Kinematics for Differential Drive Robots
  • Inverse Kinematics of Differential Drive Robots
  • Odometry-based
• Nonlinear uncertainty model associated with a robot's position over time (The Banana Distribution is Gaussian)

IMU

• 1. Global References
• 2. Accelerometer Model
• 3. Gyroscope Model
• 4. Attitude from gravity (Tilt)
  • 4.1. Solving R xyz for the Pitch and Roll Angles
  • 4.2 Solving R yxz for the Pitch and Roll Angles
• Expressing IMU reading with Quaternion
• 5. Quaternion from Accelerometer
• 6. Quaternion Integration
  • 6.1. Numerical Solution
  • 6.2. Closed-form Solution
• 7.1 Quaternion Derivative
• Relationship Between Euler-Angle Rates and Body-Axis Rates
• Complementary Filter
• Quaternion-Based Complementary Filter
• Accelerometer-Based Correction
• Attitude from angular rate (Attitude propagation)
• IMU Integration
• Noise Spectral Density
• Signal-to-noise Ratio
• Allan Variance curve
  • Variance
  • M-sample variance
  • Allan Variance
• Autoregressive model
• Madgwick Orientation Filter
• Mahony Orientation Filter
• Simulating IMU Measurements
• IMU Propagation Derivations
  • The IMU Noise Model
    • Additive "White Noise"
    • Bias
    • The Noise Model Parameters in Kalibr
  • How to Obtain the Parameters for your IMU
    • From the Datasheet of the IMU
    • From the Allan standard deviation (AD)
  • Kalibr IMU Noise Parameters in Practice
• IMU Noise Model
• The standard deviation of the discrete-time noise process
  • Example:
  • Example:
  • Note:

Apriltag

• Apriltag
• Generating Tag
• Apriltag ROS
• Detecting Apriltag

Kalibr

• Datasets and Calibration Targets
• Supported Camera Models and Distortion
• Camera Calibration
• Camera IMU Calibration

ROS1

• 1. Names
  • 1.1 Graph Resource Names
    • 1.1.1 Resolving Name
  • 1.2 Package Resource Names
• 2. Remapping Arguments
  • 3. Special keys
• NodeHandles
  • Publishers and Subscribers
• Roslaunch
• URDF
• Publishing the State
• ROS best practices
• move_base
• ROS Odometery Model
• ROS State Estimation
• EKF Implementations
• Differential Drive Wheel Systems

ROS2

• Installation
• Configuration
  • Domain ID.
  • ROS_AUTOMATIC_DISCOVERY_RANGE
  • Remapping
  • Launching nodes
• Colcon
• Using Xacro
• Creating a launch file
  • Python launch file
  • C++ launch file
  • Managing launch file
  • Substitutions
  • Event handlers
• Nav2 - ROS 2 Navigation Stack
• teleop_twist_keyboard

Gazebo

• Gazebo Versions
• Installation
• Building a model
  • Links
    • Inertia Matrix
  • Visual and collision
  • Connecting links together (joints)
• Building world
  • Physics
  • Plugins
    • Physics
    • User-commands
    • Scene-broadcaster
  • GUI
    • World control plugin
    • World stats plugin
    • Entity tree
• Moving the robot
• Sensors
  • IMU sensor
  • Lidar sensor
• Spawn URDF
• ROS 2 integration

ROS2 Gazebo Integration

• Installation of ros_gz
  • Launch Gazebo from ROS 2
• ROS2 Interaction With Gazebo
  • Bridge communication between ROS and Gazebo
  • 1. Simple Hello between ROS2 and Gazebo
  • 2. Camera Example
  • 3. Diff Drive Example
  • 4. GPU lidar
  • 5. IMU, Magnetometer
  • 6. GNSS

MarsLogger

• MarsLogger

State Estimation

• Bayes filter

• Extended Kalman Filter

• EKF Implementations

• EKF for Differential Drive Robot

• Invariant extended Kalman filter EKF

• Multi-State Constraint Kalman Filter (MSCKF)

• STATE ESTIMATION FOR ROBOTICS

• FilterPy

• Quaternion kinematics for the error-state Kalman filter

• Error State Kalman Filter (ESKF)

• Error State Extended Kalman Filter EFK ES

• Error-State-Extended-Kalman-Filter(IMU, a GNSS, and a LiDAR)

Bag of Words

• FBOW (Fast Bag of Words)
• AnyLoc: Towards Universal Visual Place Recognition

SLAM

• Active Exposure Control for Robust Visual Odometry in HDR Environments
• Pose Graph SLAM
• nano-pgo
• Factor Graph vs Pose Graph
• g2o
• GTSAM
  • Factor Graph - 5 Minutes with Cyrill
  • Georgia Tech Smoothing and Mapping Library
  • iSAM: Incremental Smoothing and Mapping
  • python-graphslam
  • GTSAM python Tutorial Robust Pose-graph Optimization
• Resilient Autonomy in Perceptually-degraded Environments
• HBA Large-Scale LiDAR Mapping Module
• Hierarchical, multi-resolution volumetric mapping (wavemap)
• kiss-icp
• TagSLAM SLAM with tags
• OpenDroneMap
• Interactive SLAM
• Volumetric TSDF Fusion of Multiple Depth Maps
• Euclidean Signed Distance Field (ESDF)
• Lidar odometry smoothing using ES EKF and KissICP for Ouster sensors with IMUs
• Multisensor-aided Inertial Navigation System (MINS)
• GLOMAP explained

Ceres Non-linear Least Squares

Visual and Inertial Odometry VIO

• Open Keyframe-based Visual-Inertial SLAM okvis
• HybVIO
• SVO Pro
• OpenVINS
  • OpenVINS Multi-Camera Extension
  • Visual-Inertial Navigation Systems: An Introduction
  • IMU Propagation Derivations openvin
• Kimera-VIO
• 3D Mapping Library For Autonomous Robots

SLAM Benchmark

• Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots
• A Comparison of Modern General-Purpose Visual SLAM Approaches
• ETH3D
• rvp group

Lidar and IMU LIO

• A Stereo Event Camera Dataset for Driving Scenarios DSEC
• FAST-LIO (Fast LiDAR-Inertial Odometry)
• incremental Generalized Iterative Closest Point (GICP) based tightly-coupled LiDAR-inertial odometry (LIO), iG-LIO
• Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction
• Robust Real-time LiDAR-inertial Initialization
• CT-LIO: Continuous-Time LiDAR-Inertial Odometry
• Lidar SLAM for Automated Driving (MATLAB learning)
• LIO-SAM
• GLIM
• Lidar-Monocular Visual Odometry

Radar SLAM

• Navtech-Radar-SLAM

Add Apriltag to loop closure

Refs: 1

Structure-from-Motion

• Robust Rotation Averaging
• Bundler
• Global Structure-from-Motion Revisited
• LightGlue
• DenseSFM
• Pixel-Perfect Structure-from-Motion
• image-matching-webui

Deep Learning based SLAM

• Gaussian Splatting
• GANeRF
• DSAC*
• Tracking Any Point (TAP)
• image-matching-benchmark
• Local Feature Matching at Light Speed
• Hierarchical Localization
• instant-ngp
• NeRF-SLAM
• DROID-SLAM
• ACE0
• A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets -
• DoubleTake:Geometry Guided Depth Estimation
• Mitigating Motion Blur in Neural Radiance Fields with Events and Frames
• LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry
• MegaScenes: Scene-Level View Synthesis at Scale
• Intrinsic Image Diffusion for Indoor Single-view Material Estimation
• Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels
• Detector-Free Structure from Motion
• Continuous 3D Perception Model with Persistent State
• MegaSam: Accurate, Fast and Robust Casual Structure and Motion from Casual Dynamic Videos
• Mast3r Slam with Rerun
• Stereo Any Video:Temporally Consistent Stereo Matching
• Multi-view Reconstruction via SfM-guided Monocular Depth Estimation
• UniK3D: Universal Camera Monocular 3D Estimation
• Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera
• Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
• VGGT: Visual Geometry Grounded Transformer
• MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM
• Speedy MASt3R
• CURL-MAP

Gaussian Splat

• Morpheus Text-Driven 3D Gaussian Splat Shape and Color Stylization

Object Pose and Shape Estimation

• RF-DETR: SOTA Real-Time Object Detection Model

Visualization

• Layered Image Vectorization via Semantic Simplification
• Potree: WebGL based point cloud renderer

Procrustes Analysis

• Procrustes Analysis
• Wahba's Problem
• Quaternion Estimator Algorithm (QUEST)
• Kabsch Algorithm
• Umeyama Algorithm
• Iterative Closest Point (ICP)
• KISS-ICP

Lidar-Camera Calibration

• MATLAB Lidar-Camera Calibration
• ROS2 Camera Lidar Fusion
• Awesome-LiDAR-Camera-Calibration

E-Books and Refs

• Modern Robotics Mechanics, Planning, and Control (Kevin M. Lynch, Frank C. Park)
• Modern Robotics Mechanics, Planning, and Control (Instructor Solution Manual, Solutions )
• MODERN ROBOTICS MECHANICS, PLANNING, AND CONTROL (Practice Exercises)
• Basic Knowledge on Visual SLAM: From Theory to Practice, by Xiang Gao, Tao Zhang, Qinrui Yan and Yi Liu
• STATE ESTIMATION FOR ROBOTICS (Timothy D. Barfoot)
• SLAM for Dummies
• VSLAM Handbook
• SLAM Handbook
• Matrix Calculus (for Machine Learning and Beyond)
• Reinforcement Learning: A Comprehensive Overview