Search Results for "slam"
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Showing 34 open source projects for "slam"
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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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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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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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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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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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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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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. -
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Kimera-VIO
Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation
Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation. 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. -
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PythonRobotics
Python sample codes and textbook for robotics algorithms
PythonRobotics is a Python code collection and textbook for learning robotics algorithms through readable examples. It covers practical topics such as localization, mapping, path planning, path tracking, control, SLAM, and autonomous navigation. The project is written to make each algorithm’s core idea easy to understand, rather than hiding the logic behind large frameworks. It keeps dependencies minimal so learners can focus on the math, implementation, and behavior of each robotics method. Visual examples and simulations help users see how algorithms move, estimate, plan, and react. ... -
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MATLAB Robotics
MATLAB sample codes for mobile robot navigation
...The repository brings together a wide range of robotics functionality, including path planning, control, localization, and mapping. It provides practical algorithmic implementations such as A*, RRT, Kalman filters, particle filters, and SLAM, making it a valuable resource for students, researchers, and developers learning or prototyping robotics applications. The code is written to be accessible and easy to adapt, enabling experimentation with both classical and modern robotics techniques. By using MATLAB’s visualization and numerical capabilities, the repository offers intuitive demonstrations of algorithms in action. ... -
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SLAM++
high-performance nonlinear least squares solver for graph problems
SLAM++ is a minimalist implementation of incremental nonlinear least squares, containing lighting fast implementation of linear algebra on sparse block matrices. It is aimed for use in 3D reconstruction or robotics. -
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IGVC IITK Data
Data useful for testing autonomous navigation algorithms
...For the relevant code, see our GitHub repositories. (https://github.com/igvc-iitk). The recorded data is used for testing various algorithms related to Computer Vision, SLAM, Motion Planning etc.
