Awesome GitHub RepositoriesSensor Synchronizations

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

SpatialLM ist ein Framework für räumliche Modellierung, das Large Language Models nutzt, um monokulare Videos und Sensordaten in strukturierte semantische Innenraumkarten zu transformieren. Es fungiert als System zur Schätzung von Raumlayouts und als semantischer Parser für Punktwolken, der rohe geometrische Daten in Repräsentationen architektonischer Elemente und Objektkategorien umwandelt. Das Projekt gleicht multimodale Sensoreingaben mit linguistischen Tokens ab, wodurch ein Sprachmodell als Reasoning-Engine zur Ableitung der Raumtopologie dienen kann. Es verwendet Mechanismen, um 3D-Punktwolken und 2D-Bildsequenzen in diskrete Tokens und strukturierte räumliche Encodings zu konvertieren, die anschließend in architektonische Layouts decodiert werden. Das Framework deckt 3D-Szenenanalyse und Objekterkennung ab, um Möbelstücke mittels Bounding Boxes und semantischen Labels zu identifizieren. Zudem bietet es Werkzeuge für das Umweltverständnis von Robotern, indem es Sensordaten verarbeitet, um semantische Karten für die autonome Navigation zu erstellen.

This project is a technical reference guide and sensor-based robotics manual focused on the theoretical foundations and practical implementation of Simultaneous Localization and Mapping. It serves as a knowledge base for spatial AI, covering the integration of deep learning and semantic rendering to create intelligent systems for open world environments. The resource provides guidance on integrating multi-modal sensor data from cameras, LiDAR, radar, and inertial sensors for localization and mapping. It also establishes a bibliographic standard for robotics research by providing systems for m

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