Displaying 1 to 20 from 85 results

maplab - An open visual-inertial mapping framework.

  •    C++

This repository contains maplab, an open, research-oriented visual-inertial mapping framework, written in C++, for creating, processing and manipulating multi-session maps. On the one hand, maplab can be considered as a ready-to-use visual-inertial mapping and localization system. On the other hand, maplab provides the research community with a collection of multi-session mapping tools that include map merging, visual-inertial batch optimization, and loop closure. Furthermore, it includes an online frontend, ROVIOLI, that can create visual-inertial maps and also track a global drift-free pose within a localization map.

g2o - g2o: A General Framework for Graph Optimization

  •    C++

g2o is an open-source C++ framework for optimizing graph-based nonlinear error functions. g2o has been designed to be easily extensible to a wide range of problems and a new problem typically can be specified in a few lines of code. The current implementation provides solutions to several variants of SLAM and BA. A wide range of problems in robotics as well as in computer-vision involve the minimization of a non-linear error function that can be represented as a graph. Typical instances are simultaneous localization and mapping (SLAM) or bundle adjustment (BA). The overall goal in these problems is to find the configuration of parameters or state variables that maximally explain a set of measurements affected by Gaussian noise. g2o is an open-source C++ framework for such nonlinear least squares problems. g2o has been designed to be easily extensible to a wide range of problems and a new problem typically can be specified in a few lines of code. The current implementation provides solutions to several variants of SLAM and BA. g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems (02/2011).

slambook

  •    C++

This is the code written for my new book about visual SLAM called "14 lectures on visual SLAM" which was released in April 2017. It is highy recommended to download the code and run it in you own machine so that you can learn more efficiently and also modify it. The code is stored by chapters like "ch2" and "ch4". Note that chapter 9 is a project so I stored it in the "project" directory. If you have any questions about the code, please add an issue so I can see it. Contact me for more information: gao dot xiang dot thu at gmail dot com.




evo - Python package for the evaluation of odometry and SLAM

  •    Python

This package provides executables and a small library for handling, evaluating and comparing the trajectory output of odometry and SLAM algorithms. See here for more infos about the formats.

LIO-SAM - LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

  •    C++

A real-time lidar-inertial odometry package. We strongly recommend the users read this document thoroughly and test the package with the provided dataset first. A video of the demonstration of the method can be found on YouTube.

Kimera - Index repo for Kimera code

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Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. Kimera is partially funded by ARL DCIST, ONR RAIDER, MIT Lincoln Laboratory, and “la Caixa” Foundation (ID 100010434), LCF/BQ/AA18/11680088 (A. Rosinol).

Kimera-VIO - Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation.

  •    C++

For evaluation plots, check our jenkins server. Kimera-VIO is a Visual Inertial Odometry pipeline for accurate State Estimation from Stereo + IMU data. It can optionally use Mono + IMU data instead of stereo cameras.


TEASER-plusplus - A fast and robust point cloud registration library

  •    C++

TEASER++ is a fast and certifiably-robust point cloud registration library written in C++, with Python and MATLAB bindings. Left: correspondences generated by 3DSmoothNet (green and red lines represent the inlier and outlier correspondences according to the ground truth respectively). Right: alignment estimated by TEASER++ (green dots represent inliers found by TEASER++).

rtabmap - RTAB-Map library and standalone application

  •    C++

RTAB-Map library and standalone application. For more information, visit the RTAB-Map's home page or the RTAB-Map's wiki.

awesome-robotic-tooling - Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace: https://freerobotics

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To stop reinventing the wheel you need to know about the wheel. This list is an attempt to show the variety of open and free tools in software and hardware development, which are useful in professional robotic development. Your contribution is necessary to keep this list alive, increase the quality and to expand it. You can read more about it's origin and how you can participate in the contribution guide and related blog post. All new project entries will have a tweet from protontypes.

mrpt - :zap: The Mobile Robot Programming Toolkit (MRPT)

  •    C++

Mobile Robot Programming Toolkit (MRPT) provides C++ libraries aimed at researchers in mobile robotics and computer vision. Libraries include SLAM solutions, 3D(6D) geometry, SE(2)/SE(3) Lie groups, probability density functions (pdfs) over points, landmarks, poses and maps, Bayesian inference (Kalman filters, particle filters), image processing, obstacle avoidance, etc. MRPT also provides GUI apps for Stereo camera calibration, dataset inspection, and much more. See this PPA for nightly builds from the develop branch, or this one for stable releases.

LeGO-LOAM - LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

  •    C++

An updated lidar-initial odometry package, LIO-SAM, has been open-sourced and available for testing. You can use the following commands to download and compile the package.

cartographer - Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations

  •    C++

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations. You can find information about contributing to Cartographer at our Contribution page.

loam_velodyne - Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar

  •    C++

Ask questions here. Issues #71 and #7 address this problem. The current known solution is to build the same version of PCL that you have on your system from source, and set the CMAKE_PREFIX_PATH accordingly so that catkin can find it. See this issue for more details.

A-LOAM - Advanced implementation of LOAM

  •    C++

A-LOAM is an Advanced implementation of LOAM (J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), which uses Eigen and Ceres Solver to simplify code structure. This code is modified from LOAM and LOAM_NOTED. This code is clean and simple without complicated mathematical derivation and redundant operations. It is a good learning material for SLAM beginners. Follow Ceres Installation.

gradslam - gradslam is an open source differentiable dense SLAM library for PyTorch

  •    Python

gradslam is a fully differentiable dense SLAM framework. It provides a repository of differentiable building blocks for a dense SLAM system, such as differentiable nonlinear least squares solvers, differentiable ICP (iterative closest point) techniques, differentiable raycasting modules, and differentiable mapping/fusion blocks. One can use these blocks to construct SLAM systems that allow gradients to flow all the way from the outputs of the system (map, trajectory) to the inputs (raw color/depth images, parameters, calibration, etc.). You should see the version number displayed.

open_vins - An open source platform for visual-inertial navigation research.

  •    C++

Welcome to the OpenVINS project! The OpenVINS project houses some core computer vision code along with a state-of-the art filter-based visual-inertial estimator. The core filter is an Extended Kalman filter which fuses inertial information with sparse visual feature tracks. These visual feature tracks are fused leveraging the Multi-State Constraint Kalman Filter (MSCKF) sliding window formulation which allows for 3D features to update the state estimate without directly estimating the feature states in the filter. Inspired by graph-based optimization systems, the included filter has modularity allowing for convenient covariance management with a proper type-based state system. Please take a look at the feature list below for full details on what the system supports. ov_secondary - This is an example secondary thread which provides loop closure in a loosely coupled manner for OpenVINS. This is a modification of the code originally developed by the HKUST aerial robotics group and can be found in their VINS-Fusion repository. Here we stress that this is a loosely coupled method, thus no information is returned to the estimator to improve the underlying OpenVINS odometry. This codebase has been modified in a few key areas including: exposing more loop closure parameters, subscribing to camera intrinsics, simplifying configuration such that only topics need to be supplied, and some tweaks to the loop closure detection to improve frequency.






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