cd /home/$USER/workspace/
git clone git@github.com:behnamasadi/robotic_notes.git
vcpkg is configured as a git submodule. Initialize it:
cd /home/$USER/workspace/robotic_notes
git submodule update --init --recursive
set the path:
export VCPKG_ROOT=$PWD/vcpkg
export PATH=$VCPKG_ROOT:$PATH
Setting VCPKG_ROOT tells vcpkg where your vcpkg instance is located.
Install required system dependencies for vcpkg (on Linux):
sudo apt-get install -y bison flex build-essential cmake autoconf autoconf-archive automake libtool libltdl-dev libx11-dev libxft-dev libxext-dev libxtst-dev libxrandr-dev ninja-build pkg-config
Now you can run:
cmake -S . -B build \
-DCMAKE_TOOLCHAIN_FILE=./vcpkg/scripts/buildsystems/vcpkg.cmake \
-DCMAKE_BUILD_TYPE=Release \
-DVCPKG_TARGET_TRIPLET=x64-linux-release
The VCPKG_TARGET_TRIPLET=x64-linux-release option ensures vcpkg only builds release packages, which significantly reduces build time (especially for large packages like OpenCV) and disk space usage. This is already configured in CMakeLists.txt, but you can explicitly set it as shown above.
cmake --build build --parallel
conda create -n robotic_notes
conda activate robotic_notes
conda install python=3.13
cd /home/$USER/anaconda3/envs/robotic_notes/
Create this soft link.
ln -s /home/$USER/workspace/robotic_notes /home/$USER/anaconda3/envs/robotic_notes/src
Install the python packages:
pip3 install rerun-sdk
conda install -c conda-forge opencv
pip install graphslam
conda install conda-forge::gtsam
conda install conda-forge::matplotlib
conda install conda-forge::plotly
conda install -c conda-forge jupyterlab
pip install gradio_rerun
pip install ahrs
pip install pyceres
pip install liegroups
- Configuration of Robot
- Configuration Space - (C-space )
- Degrees of freedom.
- Task Space
- Work Space
- Dexterous space:
- dof
- Topology
- Algebraic topology (playlist)
- Non-Holonomic Constraints, Pfaffian Constraints and Holonomic Constraints
- Kinematics of Differential Drive Robots and Wheel odometry
- Velocity-based (dead reckoning)
- Nonlinear uncertainty model associated with a robot's position over time (The Banana Distribution is Gaussian)
- 1. Global References
- 2. Accelerometer Model
- 3. Gyroscope Model
- 4. Attitude from gravity (Tilt)
- Expressing IMU reading with Quaternion
- 5. Quaternion from Accelerometer
- 6. Quaternion Integration
- 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
- Autoregressive model
- Madgwick Orientation Filter
- Mahony Orientation Filter
- Simulating IMU Measurements
- IMU Propagation Derivations
- IMU Noise Model
- The standard deviation of the discrete-time noise process
- Datasets and Calibration Targets
- Supported Camera Models and Distortion
- Camera Calibration
- Camera IMU Calibration
- 1. Names
- 2. Remapping Arguments
- NodeHandles
- Roslaunch
- URDF
- Publishing the State
- ROS best practices
- move_base
- ROS Odometery Model
- ROS State Estimation
- EKF Implementations
- Differential Drive Wheel Systems
- Installation
- Configuration
- Colcon
- Using Xacro
- Creating a launch file
- Nav2 - ROS 2 Navigation Stack
- teleop_twist_keyboard
- Gazebo Versions
- Installation
- Building a model
- Building world
- Moving the robot
- Sensors
- Spawn URDF
- ROS 2 integration
- Bayes Filter
- Kalman Filter
- Extended Kalman Filter
- Extended Kalman Filter for Differential Drive Robot
- Error State Extended Kalman Filter
- Error State Extended Kalman Filter(IMU, a GNSS, and a LiDAR)
- Multi-State Constraint Kalman Filter (MSCKF)
- Quaternion kinematics for the error-state Kalman filter
- Active Exposure Control for Robust Visual Odometry in HDR Environments
- Pose Graph SLAM from Scratch
- nano-pgo
- g2o
- Factor Graph GTSAM iSAM2
- 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
- Zero-Shot Point Cloud Registration
- Add Apriltag to loop closure
- Navtech Radar SLAM
- Procrustes Analysis
- Wahba's Problem
- Quaternion Estimator Algorithm (QUEST)
- Kabsch Algorithm
- Umeyama Algorithm
- Iterative Closest Point (ICP)
- Open Keyframe-based Visual-Inertial SLAM okvis
- HybVIO
- SVO Pro
- OpenVINS
- Error State Kalman Filter VIO (ESKF-VIO)
- Kimera-VIO
- 3D Mapping Library For Autonomous Robots
- Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots
- A Comparison of Modern General-Purpose Visual SLAM Approaches
- ETH3D
- rvp group
- 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
- Structure from Motion from Scratch
- Robust Rotation Averaging
- Bundler
- Noah Snavely Reprojection Error
- Global Structure-from-Motion Revisited
- LightGlue
- DenseSFM
- Pixel-Perfect Structure-from-Motion
- image-matching-webui
- 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
- Procrustes Analysis
- Wahba's Problem
- Quaternion Estimator Algorithm (QUEST)
- Kabsch Algorithm
- Umeyama Algorithm
- Iterative Closest Point (ICP)
- KISS-ICP
- 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
