Simple SLAM Implementation

This project is a basic implementation of SLAM using a simulated 2D LiDAR. The goal is to demonstrate the fundamental principles of SLAM, including robot pose estimation and optimization using graph.

Features

• 2D LiDAR Simulation: A virtual LiDAR sensor is simulated, providing distance measurements to surrounding objects. This data is used to map the environment and estimate the robot's pose.
• Robot Pose Optimization: The robot's pose (position and orientation) is optimized using noisy landmarks and noisy robot positions.
• Remote CPP optimizer:
  • 1. Build cpp folder: Example of using conan:
       • conan install . --output-folder=./build -s compiler.cppstd=gnu20 -s compiler.version=13 --build=missing
       • cmake --preset conan-release -DWITH_CUDA=OFF [ON/OFF]
       • cmake --build --preset conan-release
  • 2. Run: graph_optimizer HOST PORT ITERATIONS PIPELINE SOLVER (./bin/graph_optimizer "127.0.0.1" "8888" "50" cpu eigen)
       • ITERATIONS - [int value >= 1]
       • PIPELINE: [cpu/gpu]
       • SOLVER: [cuda/eigen] (if PIPELINE == GPU => SOLVER = CUDA)
  • 3. Run python3 python/slam_main.py

Requirements

• numpy: for matrix operations

• CPP (optional, for remote optimization):

  • Eigen - for matrix operations
  • Conan 2 (optional) - packet manager
  • CUDA (optional)

Pipeline

There is 4 different pipelines:

• Python UI + Python optimizer
• Python UI + CPP optimizer
• Python UI + CPP optimizer (CUDA matrix solver)
• Python UI + Full CUDA optimizer

Key concepts

• State Representation: The robot's state is represented by 3x3 matrix: [R t] [0 1]

• Odometry: [R t] [0 1]

• Graph2d: consists of robot poses in 2d and relative landmarks observations

• OptGraph: consists of robot poses in 2d and relative landmarks observations, that used for optimization

• 2 types of edges: [ODOM, LM]

Simplification

Each point in the environment has its own ID, which is used to match landmarks

Further development

• Adding More Edge and Vertex types (2d, 3d, BA, Virtual Meas.)
• Using a map for navigation
• Performance optimization
• CUDA opt improvements