Open-source alternatives to Cartographer

Navigation2 is a ROS 2 navigation framework for autonomous mobile robots. It provides the core identity of a path planner, costmap management system, kinematic motion controller, and behavior tree orchestrator to compute collision-free routes and execute movement commands. The framework is distinguished by its use of behavior trees to coordinate modular task servers, enabling complex navigation routines and autonomous recovery actions. It supports a plugin-based architecture that allows planners and controllers to be swapped at runtime to adapt to different environments. The system covers a

ORB_SLAM2 is a visual simultaneous localization and mapping system that tracks camera movement and builds 3D environments from image data. It functions as a real-time visual odometry tool and sparse 3D reconstructor, computing the position and orientation of a camera while generating a point cloud map of a physical space. The system utilizes a camera relocalization engine to identify a camera's position within a known map after tracking failure or system restarts. It incorporates a spatial tracker to enable the precise insertion and composition of virtual 3D objects into real-world planar reg

VINS-Fusion is a multi-sensor fusion framework and visual-inertial odometry system. It integrates camera images, inertial measurement unit data, and global positioning signals through a non-linear optimization system to track the position and orientation of autonomous vehicles. The system includes a visual loop closure engine that utilizes a bag-of-words approach to recognize previously visited locations and correct trajectory drift. It further provides tools for online spatio-temporal calibration to determine the physical offset and time synchronization between cameras and inertial sensors d

Slambook is a visual SLAM framework designed for simultaneous localization and mapping. It provides an integrated system to estimate camera motion and reconstruct 3D environments using visual sensor data. The project includes a visual odometry engine to track camera movement and a dense 3D reconstruction tool for creating volumetric representations of scenes. It features a loop closure detection system to recognize previously visited locations and a pose graph optimizer to refine trajectories and ensure global map consistency. The framework covers spatial estimation and environment modeling

FAST-LIVO2 is a LiDAR-inertial odometry framework and factor-graph SLAM implementation designed for real-time robot localization and 3D mapping. It functions as a multi-sensor fusion pipeline and state estimator that integrates LiDAR, inertial, and camera inputs to track a robot's position and orientation. The system employs a tightly-coupled sensor fusion approach to maintain stable navigation, particularly in degraded environments. It utilizes a voxel-based 3D mapping tool to organize point clouds into volumetric grids, which optimizes memory usage and search speed during spatial reconstruc

This project is a comprehensive library and toolkit for simultaneous localization and mapping, designed to construct three-dimensional environment models while tracking device position. It functions as a robotics perception framework that processes data from RGB-D, stereo, and lidar sensors to enable autonomous navigation and spatial awareness. The system distinguishes itself through its focus on long-term mapping and global consistency. It employs a sophisticated loop-closure detection engine and graph-based pose optimization to identify previously visited locations and eliminate cumulative

LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph optimization engine that combines lidar scans and inertial measurement unit data to build 3D point cloud maps and estimate robot trajectories. The system integrates global position factors to align local coordinates with real-world data. It employs loop closure detection to identify previously visited locations, creating constraints in the optimization graph to correct accumulated global drift. The framework covers lidar inertial odometry, point cloud processing, and trajecto

This project is a comprehensive software framework for autonomous robot navigation, providing a collection of algorithms for path planning, motion control, and trajectory generation. It serves as a toolkit for implementing and researching navigation logic, enabling the calculation of collision-free routes and the execution of precise movement commands for autonomous mobile agents. The library distinguishes itself by integrating both global pathfinding and real-time reactive control strategies. It supports diverse planning methodologies, including graph-based heuristic searches and sampling-ba

FAST_LIO is a real-time SLAM system and LiDAR-inertial odometry package designed for simultaneous localization and mapping. It functions as a state estimation engine and 3D mapping tool that fuses LiDAR point clouds with inertial measurement unit data to provide robust robot state estimation. The system utilizes a tightly-coupled sensor fusion approach with an iterative Kalman filter to estimate position and orientation. It distinguishes itself through direct point-to-plane matching, which calculates odometry by matching raw lidar points to the map surface without manual geometric feature ext

This project is a set of exercise solutions and implementation guides for visual simultaneous localization and mapping. It provides a collection of worked code examples and mathematical solutions designed to translate theoretical localization and mapping concepts into practical implementations. The repository serves as a technical companion for academic study, featuring worked answers to SLAM exercises and a system for tracking typographical and technical corrections to maintain the accuracy of the associated written work. The codebase covers spatial mathematics and robotics geometry, includ

ORB_SLAM3 is a visual-inertial SLAM library designed for real-time simultaneous localization and mapping. It provides a framework for tracking camera movement and building 3D maps of environments using monocular, stereo, or RGB-D cameras combined with inertial sensors. The system features a multi-map fusion engine capable of merging separate spatial sessions into a single seamless representation of an environment. It includes specialized processing for wide-angle and fisheye lenses to expand the visual field of view for spatial tracking. The library covers a broad range of spatial intelligen

iNAV is flight control firmware designed to manage the stability, movement, and flight dynamics of multirotors, fixed-wing aircraft, and rovers. It serves as an autonomous flight controller and GPS navigation software capable of executing waypoint missions and maintaining position and altitude hold. The system integrates GPS and compass data to enable autonomous navigation and automatic return to home sequences during signal loss or sensor failure. It includes an on-screen display generator to render real-time vehicle status and warnings directly onto a pilot video feed, alongside a flight te

Open_vins is a visual-inertial odometry framework and SLAM system designed for robotic state estimation. It uses an Extended Kalman Filter to fuse high-frequency inertial sensor data with visual feature tracks to estimate the position and orientation of a moving device. The system features a sensor calibration suite for calculating intrinsic and extrinsic parameters, as well as temporal offsets between cameras and inertial measurement units. It includes a manifold interpolator that uses B-Spline curves over the special Euclidean group to produce smooth trajectory paths between discrete pose e

VINS-Mono is a monocular visual-inertial odometry system and loop closure SLAM framework. It functions as a real-time state estimator that fuses data from a single camera and an inertial measurement unit to determine a robot's position and orientation. The project includes a non-linear optimizer for robotics and tools for sensor calibration. The system distinguishes itself through online sensor calibration, which automatically determines spatial extrinsics and temporal offsets between the camera and inertial unit during operation. It also incorporates rolling shutter distortion correction to

This project is a robotics software package designed for simultaneous localization and mapping, providing a framework for visual-inertial odometry and environmental mapping. It functions as a middleware-integrated library that enables autonomous mobile robots to estimate their position and orientation by processing sensor data within modular software systems. The library distinguishes itself by utilizing hardware-accelerated processing to perform feature tracking and odometry calculations on dedicated graphics hardware. It maintains spatial accuracy through graph-based optimization and statis

PX4-Autopilot is a professional-grade flight control software stack designed for autonomous unmanned vehicles, including multicopters, fixed-wing aircraft, and vertical takeoff and landing platforms. It operates as a modular, real-time framework that decouples flight control logic from hardware drivers through a publish-subscribe middleware architecture. The system utilizes a deterministic microkernel runtime to execute time-critical flight control loops and sensor fusion tasks, ensuring stable navigation and vehicle operation. The platform distinguishes itself through a parameter-driven conf

evo is a Python framework for the evaluation of SLAM algorithms, robot odometry, and trajectory data. It serves as an analysis library for measuring drift and precision by calculating absolute and relative pose errors between estimated paths and ground truth references. The project provides a geometric alignment framework to correct rotation, translation, and scale between spatial trajectories, ensuring consistent error measurement. It includes specialized tools for odometry drift analysis and the processing of robotics data, including the ability to extract trajectory information from ROS ba

This project is a computer vision system for monocular depth estimation and 3D point cloud generation. It provides a supervised depth learning framework and a depth predictor capable of estimating spatial distance and disparity from single 2D images using pretrained neural networks. The system includes tools to transform 2D depth images into 3D point clouds via pixel coordinate backprojection and converts 3D point cloud data into 2D depth maps. It utilizes a training pipeline that supports model fine-tuning and hyperparameter optimization. The library covers broader capabilities in spatial a

pyslam is a framework for Simultaneous Localization and Mapping that combines Python flexibility with C++ performance. It is a sparse SLAM implementation designed to map environment geometry and track device location by processing image frames into 3D points. The project features a bridge for exposing high-performance C++ classes to Python scripts using zero-copy memory sharing. This integration allows for switching between a scripting interface for rapid prototyping and a compiled core for execution speed. The system includes a spatial map optimizer to refine 3D point and camera pose estima

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

This project is an educational resource and toolkit for implementing Bayesian estimation and Kalman filters in Python. It provides a framework for constructing linear and non-linear filters to estimate the state of dynamic systems by combining noisy sensor data with mathematical process models. The library focuses on probabilistic state estimation, utilizing recursive Bayesian updating and state-space mathematical modeling to refine beliefs about system states. It includes utilities for simulating dynamic systems, allowing users to generate synthetic trajectories and sensor observations to va

Autonomous ground robots (2WD, 4WD, Ackermann Steering, Mecanum Drive)

3d printable Robot arm See the hackaday project page https://hackaday.io/project/3800-3d-printable-robot-arm and http://www.chaozlabs.de/ for more infos.

K-Bot is an open-source humanoid robot from K-Scale Labs. It is our attempt to build a future where robots are owned by everyone. We welcome your feedback, issues and pull requests on Github or through our Discord . Docs · Discord · Bounties

Mars Rover Curiosity/Perseverance inspired Rover

#BCN3D MOVEO

Thank you everyone for your interest and support over the last 6 years for this project!