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LEGO-SLAM is a light weight stereo visual SLAM system

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LEGO-SLAM

LEGO-SLAM is a light weight stereo visual SLAM system which contains multi hand-made modules, such as a frontend with the pyramid KLT optical flow method based on the Gauss-Newton algorithm & OpenCV ParallelLoopBody and a backend with the graph-based Levenberg-Marquardt optimization algorithm (LEGO or g2o (optional)). Generally speaking, LEGO-SLAM is a silding-window stereo visual SLAM framework for learning and practicing visual SLAM theories. Now, the optimization algorithm in the backend (LEGO) only depends on the Levenberg-Marquardt algorithm and it will include more algorithms in the future, such as Dog-leg. Moreover, the loop-closure module will be implemented in the near future. This version is only tested on Ubuntu 18.04.

Installation

Prerequisites

  • GCC and CMake

  • Eigen3: Linear algebra

    On Ubuntu 18.04, we can install CMake and Eigen3 following:

    # gcc and cmake 
    sudo apt-get install gcc cmake 
    # Eigen3 
    sudo apt-get install libeigen3-dev 
  • OpenCV 3: Only for image visualization and I/O (Version >= 3.2)

  • Sophus: Lie groups and Lie algebra based on Eigen

  • Pangolin: Display and interactive visualization based on OpenGL

  • glog, gtest, gflags

  • CSparse: Sparse matrix computing

    # suitesparse 
    sudo apt-get install libsuitesparse-dev
  • g2o (optional): A general framework for graph optimization

  • LEGO: A light weight graph-based optimization library

Build and Run

  • Build

    We can build and run LEGO-SLAM from this repository and follow:

    git clone https://github.com/zha0ming1e/LEGO-SLAM.git 
    cd LEGO-SLAM/ 
    mkdir build/ 
    cd build/ 
    cmake .. && make -j6 

    Now we have already built the LEGO-SLAM and then we can test and run it.

  • Test and Run: There will be 2 executable files under the bin/ folder

    • Test: Test triangulation in 3D

      # test triangulation 
      ./bin/legoslam_test_triangulation 
      
      [==========] Running 1 test from 1 test case.
      [----------] Global test environment set-up.
      [----------] 1 test from LEGOSLAMTest
      [ RUN      ] LEGOSLAMTest.Triangulation
      [       OK ] LEGOSLAMTest.Triangulation (0 ms)
      [----------] 1 test from LEGOSLAMTest (0 ms total)
      
      [----------] Global test environment tear-down
      [==========] 1 test from 1 test case ran. (1 ms total)
      [  PASSED  ] 1 test.
    • Run: Modify the config files under the config/ folder, such as the path of KITTI datasets and then run LEGO-SLAM

      # KITTI 00 
      ./bin/legoslam_kitti_feature ./config/kitti_00.yaml 
      # KITTI 05 
      ./bin/legoslam_kitti_feature ./config/kitti_05.yaml

Results

This version only supports KITTI odometry datasets.

  • KITTI 00

    kitti_00

  • KITTI 05

    kitti_05

  • Demo

    demo

References

  • ORB-SLAM2: Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities
  • ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
  • DSO: Direct Sparse Odometry
  • LDSO: DSO with SIM(3) pose graph optimization and loop closure
  • VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
  • VINS-Course: VINS-Mono code without Ceres or ROS
  • slambook2: Edition 2 of the slambook

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