hySLAM is a hybrid SLAM/SfM system designed for mapping

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Deep Learning hyslam
Overview

HySLAM Overview

hySLAM is a hybrid SLAM/SfM system designed for mapping. The system is based on ORB-SLAM2 with some modifications and refactoring.

Raúl Mur-Artal and Juan D. Tardós. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, 2017.

ORB-SLAM2 original code: ORB-SLAM2_repo

Modifications:

  1. Support for multiple cameras.
  2. Addition of a Multi-map, recursive data structure: a recursive tree structure is used to handle sub-maps. Sub-maps can be optionally registered with their parent to make keyframes and map points accessible to the parent or the sub-map can be kept private
  3. Trajectory tracking: Per-frame camera trajectories are explicitly recorded as SE(3) transformations relative to reference keyframes, whose positions are continuously updated via optimization
  4. Extensive code refactoring including converting Tracking to a state-machine, conversion of Mapping to a job based, parallel module, and addition of a separate Feature Extraction thread.

Example Use

hySLAM was used as the basis for a dual-camera SLAM system to map visually repetitive ecosystems such as grasslands, where conventional Strucuture from Motion techniques are unreliable. The dual-camera SLAM system uses a forward-facing stereocamera to provide localization information while a downward facing high-resolution "documentation" camera is used to record the ecosystem. Conventional SLAM is used to analyze the forward-facing stereocamera video. The trajectory of the stereocamera is then used to guide localization and mapping from the documentation camera as illustrated in the figure below: dc_overview

The dual-camera SLAM system allows reliable mapping of repetitive ecosystems as illustrated below: recon_examples A: Accurately reconstructed campus lawn using dual camera SLAM. B: SfM failure due to visual aliasing (blue squares represent aligned images). The three lines of images (highlighted in red) should be parallel but instead converge on a single point in the reconstruction

Dependencies:

  1. pangolin
  2. DBoW2
  3. OpenCV

Installation

  1. in Thirdparty, compile and install g2o: cmake .. make -jX sudo make install sudo ldconfig
  2. compile and install hyslam in main CMakeLists set opencv directory cmake .. make -jX sudo make install
  3. build binary vocabulary: ./tools/bin_vocabulary
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Comments
  • How did this repo support multi camera case?

    How did this repo support multi camera case?

    Hi,

    Thanks for your excellent work. In the README, it said the repo support multi-camera cases. But I could not figure out how does it support that by looking at some code of yours.....

    Could you please explain a little bit about it while you're free? Thanks

    Deshun

    opened by hitdshu 0
Owner
Brian Hopkinson
Associate Professor, Applications of Computer Vision and Machine Learning to Environmental Science
Brian Hopkinson
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