Search Results for "slam"
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Showing 12 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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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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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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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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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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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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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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Swing Java Application manages software requirements and associated functional tests, including results, showing requirements/tests traceability at all time. Requirements and tests are formatted as in text processor; print-ready documents are generated.
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A complete cleanup (for readability and ease of coding) and doccumentation of the original SMAUG 1.4a codebase, SLAM-GTP is also a minor re-write, bringing new features to the codebase, while also fixing and/or adding functionality to existing ones.
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