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Showing 55 open source projects for "slam"
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Isaac ROS Visual SLAM
Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM
Discover a faster, easier way to build advanced AI robotics applications with the NVIDIA Isaac™ ROS collection of accelerated computing packages and AI models, bringing NVIDIA acceleration to ROS developers everywhere. Isaac ROS Visual SLAM provides a high-performance, best-in-class ROS 2 package for VSLAM (visual simultaneous localization and mapping). This package uses one or more stereo cameras and optionally an IMU to estimate odometry as an input to navigation. It is GPU-accelerated to provide real-time, low-latency results in a robotics application. VSLAM provides an additional odometry source for mobile robots (ground-based) and can be the primary odometry source for drones. ... -
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SLAM in Autonomous Driving Book
SLAM Technology in Autonomous Driving
SLAM in Autonomous Driving Book is the open-source code companion to the book SLAM in Autonomous Driving. It focuses on localization and mapping systems that combine LiDAR, IMU, GNSS, and vehicle motion data. The material begins with geometry, kinematics, Kalman filtering, inertial navigation, preintegration, and graph optimization. It then implements point-cloud processing, nearest-neighbor structures, ICP, NDT, scan matching, occupancy grids, and loop closure. -
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lidarslam_ros2
ROS 2 package of 3D lidar slam using ndt/gicp registration
ROS 2 package of 3D lidar slam using ndt/gicp registration and pose-optimization. ros2 slam package of the frontend using OpenMP-boosted gicp/ndt scan matching and the backend using graph-based slam. -
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rtabmap
RTAB-Map library and standalone application
RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. The loop closure detector uses a bag-of-words approach to determine how likely a new image comes from a previous location or a new location. When a loop closure hypothesis is accepted, a new constraint is added to the map’s graph, then a graph optimizer minimizes the errors in the map. -
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Slambook2
edition 2 of the slambook
slambook2 is the updated companion codebase for the second edition of 14 Lectures on Visual SLAM: From Theory to Practice. It preserves the chapter-by-chapter learning structure while revising examples for newer systems and libraries. The repository teaches the mathematical and programming foundations required to understand visual SLAM implementations. Its C++ exercises cover geometry, Lie theory, cameras, image processing, optimization, visual odometry, mapping, and loop closure. ... -
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CoordinateTransformations.jl
A fresh approach to coordinate transformations
...Transformations are designed to be light-weight and efficient enough for, e.g., real-time graphical applications, while support for both explicit and automatic differentiation makes it easy to perform optimization and therefore ideal for computer vision applications such as SLAM (simultaneous localization and mapping). -
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Beginner JavaScript
Slam Dunk JavaScript
Beginner JavaScript is the companion repository to a hands-on course that teaches modern JavaScript from the ground up using practical exercises and real code. It focuses on fundamentals—variables, types, functions, arrays, objects, DOM manipulation, and events—while introducing ES6+ features in approachable steps. The materials encourage learning by doing, with interactive challenges, starter files, and solutions you can compare against your own attempts. Clear explanations and incremental... -
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The Hypersim Dataset
Photorealistic Synthetic Dataset for Holistic Indoor Scene
...It provides richly annotated renderings—RGB, depth, surface normals, instance and semantic segmentations, and material/lighting metadata—produced from high-fidelity virtual environments. The dataset spans diverse furniture layouts, room types, and camera trajectories, enabling robust training for geometry, segmentation, and SLAM-adjacent tasks. Rendering pipelines and utilities allow researchers to reproduce sequences, generate novel views, or extract task-specific supervision. Because the data are perfectly labeled and controllable, Hypersim is well suited for pretraining and for studying domain transfer to real imagery. The repository acts as both a dataset index and a set of scripts for downloading, managing, and evaluating on standardized splits. -
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Theseus
A library for differentiable nonlinear optimization
Theseus is a library for differentiable nonlinear optimization that lets you embed solvers like Gauss-Newton or Levenberg–Marquardt inside PyTorch models. Problems are expressed as factor graphs with variables on manifolds (e.g., SE(3), SO(3)), so classical robotics and vision tasks—bundle adjustment, pose graph optimization, hand–eye calibration—can be written succinctly and solved efficiently. Because solves are differentiable, you can backpropagate through optimization to learn cost... -
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Tomb
the Crypto Undertaker
Tomb is a minimalistic command-line tool for creating and managing encrypted “tombs,” which are files that act as mountable, private storage vaults. Under the hood it uses Linux dm-crypt with LUKS for strong, disk-level encryption, and separates the key material from the vault itself to improve security workflows. Keys can be protected with passphrases and even hidden via steganography inside innocuous files, giving you options for concealment and off-device storage. The interface is... -
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GraphView
Bundle Adjustment Graph Visualisation
A simple OpenGL ES viewer of Bundle Adjustment (BA) graphs. This is a tool, useful with SLAM ++. -
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The 3D Toolkit provides algorithms and methods to process 3D point clouds. In includes automatic precise registration (6D simultaneous localization and mapping, 6D SLAM) and other tools, e.g., a fast 3D viewer, plane extraction software, etc.
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slambook
Code for new book about visual SLAM
slambook is the companion code repository for the first edition of 14 Lectures on Visual SLAM: From Theory to Practice. It organizes practical C++ examples by book chapter so readers can compile, run, and modify each concept. The material progresses from Linux, CMake, rigid-body motion, Eigen, Lie groups, and camera models to complete SLAM components. It demonstrates nonlinear optimization with Ceres and g2o, feature-based visual odometry, and direct visual odometry. -
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Caesar.jl
Robust robotic localization and mapping
A multimodal/non-Gaussian robotic toolkit for localization and mapping -- reducing the barrier of entry for sensor/data fusion tasks, including Simultaneous Localization and Mapping (SLAM). Code changes are currently tracked via Github's integrated Milestone/Issues/PR system. -
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CoTracker
CoTracker is a model for tracking any point (pixel) on a video
...The repository ships with inference scripts, pretrained weights, and simple interfaces to seed points, run tracking, and export trajectories for downstream tasks. Typical uses include correspondence building, motion analysis, dynamic SLAM priors, video editing masks, and evaluation of geometric consistency in real scenes. -
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ucoslam
Visual SLAM. https://www.uco.es/investiga/grupos/ava/portfolio/ucoslam
https://www.uco.es/investiga/grupos/ava/portfolio/ucoslam/ UcoSLAM is a library for Simultaneous Localization and Mapping using keypoints that able to operate with monocular cameras, stereo cameras, rgbd cameras. Additionally, our library is fully integrated with the ArUco library for detecting squared fiducial markers. They can be placed in the environment to improve tracking. See doc at https://drive.google.com/file/d/1bFIEPKadVKKbr_SkIUxGkipIe0t1AxpN/view?usp=share_link -
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LaMa
LaMa - A Localization and Mapping library
LaMa is a C++11 software library for robotic localization and mapping developed at the Intelligent Robotics and Systems (IRIS) Laboratory at the University of Aveiro - Portugal. It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching, and two SLAM solutions (an Online SLAM and a Particle Filter SLAM). The main feature is efficiency. Low computational effort and low memory usage whenever possible. The minimum viable computer to run our localization and SLAM solutions is a Raspberry Pi 3 Model B+. We provide a fast scan-matching approach to mobile robot localization supported by a continuous likelihood field. ... -
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BTS-SIO-SLAM-CYBER-INTERVENTION
Cybersecurity intervention for BTS SIO SLAM
You will find all the files to prepare a security intervention in a BTS SIO (SLAM - development part). This training aims to highlight the role of the developer in cybersecurity. Different software vulnerabilities are presented as well as the good practices that allow limiting them. To this goal, some aspects of a secure system design are addressed. Informations and documentation are here: https://sourceforge.net/p/bts-sio-slam-cyberintervention/wiki/Home/. -
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Slam Mirror Bot
Aria/qBittorrent Telegram Mirror/Leech Bot
Slam Mirror Bot is a multipurpose Telegram Bot written in Python for mirroring files on the Internet to our beloved Google Drive. Based on python-aria-mirror-bot. -
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Introduction-to-Autonomous-Robots
Introduction to Autonomous Robots
An open textbook focusing on computational principles of autonomous robots. The source-code is released under Creative Commons 4.0 (CC-BY-NC-ND), whereas the print version is copyrighted by MIT Press. You are therefore permitted to use images and content from the book for non-commercial purposes (including teaching) with proper attribution, but you cannot post compiled versions of the book online. In order to compile a PDF of this book yourself, you either need a working implementation of... -
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libRSF
A robust sensor fusion library for online localization
The libRSF is an open source C++ library that provides several components that are required to estimate the state of a (robotic) system based on probabilistic methods. By applying the factor graph concept, well known from Graph SLAM, libRSF provides a robust solution for many sensor fusion problems. The general idea of factor graphs is to describe the state estimation problem as a graph of nodes (the state variables) that are connected by factors (measurements). The resulting graph optimization problem can be solved by applying non-linear least squares optimization. -
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Cartographer
Cartographer is a system that provides real-time localization
Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations. -
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SLAMBook-en
The English version of 14 lectures on visual SLAM
This project is the English version of “14 Lectures on Visual SLAM: From Theory to Practice,” a text and teaching resource about visual simultaneous localization and mapping (SLAM). It provides the full LaTeX source (formerly Markdown) for all 14 chapters, letting readers compile and study the material systematically. Within the repository you’ll find organized subfolders (e.g. chapters, latex, resources) containing the lecture contents, references, figures, and supporting assets for each part of the course. ... -
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gradslam
gradslam is an open source differentiable dense SLAM library
...However, learning representations for SLAM has been an open question, because traditional SLAM systems are not end-to-end differentiable. In this work, we present gradSLAM, a differentiable computational graph take on SLAM. Leveraging the automatic differentiation capabilities of computational graphs, gradSLAM enables the design of SLAM systems that allow for gradient-based learning across each of their components, or the system as a whole.