Ubuntu 16.04 下 PL-SLAM (Stereo) 的安装和使用


[TOC]

Overview

This code rubengooj/pl-slam contains an algorithm to compute stereo visual SLAM by using both point and line segment features.

  • Related Publication

    @article{gomez2017pl,
      title   = {PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments},
      author  = {Gomez-Ojeda, Ruben and Zuñiga-Noël, David and Moreno, Francisco-Angel and Scaramuzza, Davide and Gonzalez-Jimenez, Javier},
      journal = {arXiv preprint arXiv:1705.09479},
      year    = {2017}
    

Prerequisites and Dependencies

  • Basics

    sudo apt install build-essential pkg-config libboost-dev \
    libsuitesparse-dev libeigen3-dev libyaml-cpp-dev
    
  • OpenCV 3.x.x
    • I installed OpenCV 3.3.1 along with ros-kinetic
  • G2O
    • recommend version: commit id ff647bd (ff647bd7537860a2b53b3b774ea821a3170feb13)
  • MRPT/mrpt: The Mobile Robot Programming Toolkit
    • recommend version: commit id 0c3d605 (0c3d605c3cbf5f2ffb8137089e43ebdae5a55de3)
    git clone https://github.com/MRPT/mrpt.git
    git branch cg_0c3d605 0c3d605c3cbf5f2ffb8137089e43ebdae5a55de3
    git checkout cg_0c3d605
    
    # install dependencies
    sudo apt install libdc1394-22-dev libjpeg-dev libftdi-dev freeglut3-dev \
    libwxgtk3.0-dev zlib1g-dev libusb-1.0-0-dev libudev-dev libfreenect-dev \
    libavformat-dev libswscale-dev libassimp-dev libgtest-dev libpcap-dev
    
    # build & install
    mkdir build & cd build
    cmake .. & make -j4
    sudo make install
    
  • rubengooj/stvo-pl: Stereo Visual Odometry by combining point and line segment features

    git clone https://github.com/rubengooj/stvo-pl.git
    cd stvo-pl
    chmod +x build.sh
    ./build.sh
    

Note: it’s better mrpt, stvo-pl and pl-slam are in the same directory

Build

Build pl-slam

git clone https://github.com/rubengooj/pl-slam.git
chmod +x build.sh
./build.sh

Errors

  • Q: /usr/bin/ld: cannot find -lg2o_ext_csparse
    A: sudo ln -sv libg2o_csparse_extension.so libg2o_ext_csparse.so

Run

Dataset

Kitti data_odometry_gray

  • edit ~/.bashrc, and
    add export DATASETS_DIR=<path-to-data_odometry_gray>/sequences
  • copy pl-slam/config/dataset_params/kitti00-02.yaml
    to <path-to-data_odometry_gray>/sequences/00/,
    rename the yaml file to dataset_params.yaml and change it if necessary
  • source ~/.bashrc
  • edit pl-slam/config/config/config_kitti.yaml, change the value of vocabulary_p and vocabulary_l
  • run
    ./plslam_dataset 00 -c ../config/config/config_kitti.yaml -o 100 -s 1 -n 1000
    or
    ./plslam_dataset 00 -c ../config/config/config_kitti.yaml -o 100 -s 1

Result

EuRoC MH_01_easy

  • edit ~/.bashrc, and add export DATASETS_DIR=<path-to-MH_01_easy>
  • copy pl-slam/config/dataset_params/euroc_params.yaml to <path-to-MH_01_easy>/mav0/,
    rename the yaml file to dataset_params.yaml and change it if necessary
  • source ~/.bashrc
  • edit pl-slam/config/config/config_euroc.yaml, change the value of vocabulary_p and vocabulary_l
  • run ./plslam_dataset mav0 -c ../config/config/config_euroc.yaml -o 100 -s 1

Run Errors

  • the app crashed and get the following error when restart the app after close it with Ctrl+C

    DRM_IOCTL_I915_GEM_APERTURE failed: Invalid argument Assuming 131072kB available aperture size. May lead to reduced performance or incorrect rendering. get chip id failed: -1 [22] param: 4, val: 0 Segmentation fault (core dumped)

    and it fixed after reinstalling Nvidia-driver

  • the app crashed with the error Segmentation fault (core dumped) after run Frame #1600 with the KITTI data_odometry_gray dataset, but have not solved it




^