Tools tagged with "Simultaneous Localization and Mapping"

The Computer Vision Group at TUM offers a variety of datasets for researchers in computer vision, image processing, and pattern recognition. These datasets are shared to foster academic collaboration, with a request to cite the relevant publications when utilized.

Project AirSim is Microsoft's next-generation simulation platform, evolving from AirSim to provide high-fidelity virtual environments. It is designed for the comprehensive development, training, and rigorous testing of various autonomous systems. The platform supports developers in creating robust and intelligent autonomous solutions.

Hexagon is a global leader in measurement technologies, providing confidence to vital industries. It focuses on enabling customers to build, navigate, and innovate through its comprehensive solutions. The company's offerings span from precision measurement to autonomous solutions, transforming various sectors.

OpenSfM is an open-source Structure-from-Motion (SfM) pipeline developed by Mapillary. It provides tools and algorithms for reconstructing 3D models from collections of unordered images, serving as a foundational component for various geospatial and computer vision applications. The project is hosted on GitHub, encouraging community contributions and development.

Cartographer is an open-source software system designed to provide robust real-time simultaneous localization and mapping (SLAM) capabilities. It supports both 2D and 3D environments, enabling precise navigation and mapping across various platforms and sensor configurations. This system is crucial for applications requiring accurate self-localization and environmental modeling.

The KITTI Vision Benchmark Suite offers challenging real-world datasets for computer vision tasks in autonomous driving. Data is collected using a research vehicle equipped with high-resolution cameras, a Velodyne laser scanner, and a GPS system in various environments around Karlsruhe, Germany. It provides benchmarks for tasks like stereo, optical flow, visual odometry, 3D object detection, and 3D tracking.

libpointmatcher is a robust and modular C++ library designed for aligning point clouds using the Iterative Closest Point (ICP) algorithm. It provides essential functionalities for 2D and 3D mapping tasks, making it highly valuable for applications in robotics and computer vision. The library also offers convenient Python bindings for broader accessibility.

The Point Cloud Library (PCL) is a standalone, large-scale, open project for 2D/3D image and point cloud processing. It provides advanced algorithms and data structures for tasks like filtering, feature estimation, surface reconstruction, segmentation, and object recognition. PCL is designed for a wide range of applications in robotics, computer vision, and geospatial analysis.