Slam
Showing 36 of 83 projects
A 3D segment-based mapping library for robot localization, environment reconstruction, and semantics extraction using LiDAR data.
A CUDA-accelerated library for rapid 3D data processing in robotics, enabling GPU-powered SLAM, collision avoidance, and path planning.
A tightly coupled 3D LiDAR-inertial odometry and mapping system for real-time robot localization and mapping.
An efficient LiDAR-based semantic SLAM system that builds 3D semantic maps from laser scans.
An open-source 3D LIDAR-based mapping framework for semi-automatic, interactive correction of SLAM mapping failures.
A modular C++ and ROS 2 framework for building configurable LiDAR odometry and SLAM pipelines.
A modular ROS package for 3D/6D robot localization and point cloud registration using PCL, with dynamic map updates via OctoMap.
An open-source visual-inertial odometry system that estimates camera motion and sparse 3D maps from camera and IMU data.
Monocular 3D object detection and SLAM system that detects and tracks cuboids to estimate camera and object poses.
Real-time 3D semantic reconstruction library for robotics, building dense metric-semantic maps from 2D sensor data.
A Siamese neural network for LiDAR-based loop closing and localization by predicting scan overlap and relative yaw angle.
Real-time 3D semantic mapping system using a handheld RGB-D camera, built on ROS with ORB_SLAM2 and PSPNet.
ROS packages for interfacing with Velodyne 3D LIDAR sensors in robotics applications.
A learning-based approach for moving object segmentation in 3D LiDAR data, distinguishing moving vs. static objects in real-time.
A dense visual odometry and SLAM system for RGB-D cameras that estimates camera motion from consecutive depth images.
A LiDAR-based tool for constructing static maps by removing dynamic points from point cloud sequences.
A fast and robust ground segmentation algorithm for 3D LiDAR point clouds, using concentric zone-based region-wise processing.
A low-cost and accurate SLAM system that fuses Livox lidar with camera data for robust localization and mapping.
A simple, robust, and accurate 3D LiDAR SLAM system designed to just work.
A robust system for multi-LiDAR extrinsic calibration, real-time odometry, and mapping without manual intervention.
A curated collection of robotics and computer vision datasets for research and development.
A real-time visual odometry pipeline using stereo event-based cameras, leveraging Time Surfaces for direct geometric mapping and tracking.
A ROS 2 package for tightly-coupled LiDAR-inertial SLAM using NDT/GICP scan matching with loop closure.
A C++/TensorRT inference module for RangeNet++, enabling fast LiDAR semantic segmentation for robotics applications.
A ROS2-based package for Simultaneous Localization and Mapping (SLAM) using AprilTag fiducial markers.
A C++11 library for efficient robotic localization and mapping, designed for low-resource hardware like Raspberry Pi.
A full LiDAR SLAM system for static environment mapping using LiDAR with optional GPS, IMU, and odometry support.
Real-time visualization and processing tool for live 3D LiDAR data from Velodyne sensors.
ROS 2 wrapper for Stereolabs ZED cameras, providing access to depth, point clouds, object detection, and spatial mapping.
Real-time reception, recording, visualization, and processing of 3D LiDAR data from multiple manufacturers.
A curated list of top-tier publications and resources for LiDAR-Visual fusion SLAM systems.
A real-time fiducial tag system for LiDAR point clouds, robust to lighting and compatible with visual markers like AprilTags.
A ROS-based system for robot localization and mapping using ceiling or floor-mounted fiducial markers.
A ROS package extension for ORB-SLAM2 that enables saving and loading ORB feature maps for closed-circuit visual localization of autonomous vehicles.
A ROS wrapper for libviso2, enabling visual odometry for monocular and omnidirectional camera systems in robotics.
A recursive B-spline-based state estimation framework for 6-DoF LiDAR odometry, supporting LiDAR-only, LiDAR-inertial, and multi-LiDAR configurations.
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