This repository is a complete ROS2 software development kit for the Unitree Go2 quadruped robot. It provides a high-level SDK (go2_robot_sdk) with clean architecture (domain, application, infrastructure, presentation) that enables connection to the robot via WebRTC (Wi-Fi) or CycloneDDS (Ethernet), reception and decoding of real-time data (LiDAR, IMU, joint states, camera, odometry), and control commands (movement, stance, joystick). It includes multiple ROS2 packages: go2_interfaces (25+ custom message types), lidar_processor (Python and C++ nodes for LiDAR point cloud decoding and aggregation), speech_processor (ElevenLabs TTS with caching and robot playback), coco_detector (object detection using PyTorch FasterRCNN), and launch files for SLAM (slam_toolbox) and autonomous navigation (Nav2). The repository also contains URDF models, calibration files, RViz configurations, Docker support, and multi-robot namespace remapping. While primarily an application for controlling and monitoring the Go2 robot, it includes data-processing components (LiDAR decoding, IMU filtering, object detection) that give it a hybrid data-research character. The codebase demonstrates professional ROS2 development practices with modular design, abstract interfaces, and multiple language implementations.
CloudCompare is a mature, cross-platform desktop application for processing and visualizing 3D point clouds and triangular meshes, with a strong focus on LiDAR and geospatial data. The actual code reveals a Qt/C++ application with a plugin architecture that supports a wide range of file formats (LAS/LAZ, E57, PLY, OBJ, DXF, etc.) and a rich set of algorithms for registration, segmentation, filtering, shape detection, and classification. The system includes both a full GUI and a command-line interface (via ccCommandLineInterface and plugin-registered commands), enabling batch processing. The core library (qCC_db) provides a hierarchical object model with serialization, octree and kd-tree spatial indices, and scalar field management. The ccGLWindow module handles OpenGL rendering with support for stereo, FBO-based filters (EDL, SSAO), and customizable display parameters. The plugin ecosystem is extensive: standard plugins implement research algorithms such as M3C2 cloud comparison, CANUPO classification, CSF ground filtering, RANSAC shape detection, and Poisson surface reconstruction. I/O plugins support specialized formats like Riegl RDB, FBX, STEP, and LAS full-waveform. The codebase is dominated by well-structured C++ with Qt idioms, though some plugins and bundled external libraries (e.g., dxflib, qhull, RANSAC_SD) show varying maintenance patterns. Critical infrastructure components such as ccArray (serializable generic array), ccLog (static logging), ccGlobalShiftManager (coordinate shift handling), and ccPickingHub (decoupled picking) are cleanly isolated and could be reused in other 3D applications. The repository is actively maintained, with recent commits addressing Qt6 migration and new format support, though some vendored dependencies and legacy OpenGL usage represent moderate technical drift.
PiLiDAR is a DIY 360° 3D panorama scanner application for Raspberry Pi. It integrates a LiDAR (LD06/STL27L), stepper motor, IMU, and Raspberry Pi camera to capture 2D scans and photos, then processes them into colored 3D point clouds and 360° panoramas. The system is orchestrated by a main Python script (PiLiDAR.py) that controls hardware, captures data, and runs post-processing pipelines. The codebase is modular with a library of device drivers, calibration utilities, and 3D processing functions. It supports hardware PWM via curve-fitted speed control, automatic exposure and white balance calibration, Hugin-based panorama stitching, and Open3D-based point cloud registration, meshing, and visualization. The architecture is monolithic but well-organized, with clear separation of concerns. Key strengths include robust LiDAR protocol handling, reusable stepper motor control, and a comprehensive point cloud processing pipeline. The project is actively maintained with recent commits and low technical drift, though it requires specific hardware and external tools (Hugin, rpicam-still) to function.
This repository provides the `lidar` Python package, a specialized library for hydrological depression analysis of high-resolution topographic data, particularly LiDAR-derived DEMs. Its core functionality includes DEM smoothing (mean, median, Gaussian filters), sink extraction, and a novel level-set algorithm for delineating nested depression hierarchy, yielding topological and geometric properties (size, volume, depth, perimeter). The package is designed as a reusable library with a CLI (`lidar` command), a PySimpleGUI-based graphical interface, and integration with ArcGIS via a dedicated toolbox. It also includes Jupyter notebooks for tutorial and research workflows, demonstrating the complete analysis pipeline. The project is research-grade, backed by peer-reviewed publications, and follows a modular architecture with separate modules for filtering, filling, slicing, and mounts. However, some dependencies (GDAL, arcpy) and Python version constraints introduce moderate technical drift risk.
GS-SDF is a research prototype from HKU-MARS that fuses LiDAR-augmented 3D Gaussian Splatting with a neural Signed Distance Field (SDF) for photorealistic rendering and high-fidelity surface reconstruction. The system ingests data from multiple sensor modalities (LiDAR, RGB-D, stereo) via a modular data parser hierarchy supporting Replica, FAST-LIVO2, KITTI, COLMAP, Oxford Spires, and other formats. It trains a neural SDF field with eikonal and curvature regularization, then initializes and refines 3D Gaussians guided by the SDF to produce view-dependent color and geometry. The pipeline includes real-time ROS visualization (RVIZ), offline mesh extraction via a custom CUDA marching cubes implementation, and evaluation scripts for image metrics (PSNR, SSIM, LPIPS) and structural metrics (Chamfer distance, F-score). The codebase is pre-release, with many TODOs and commented-out code, but exhibits strong modularity in its data loaders, loss functions, optimizer utilities, and coordinate transformations. While primarily a research codebase, it includes application-like features such as a ROS node with interactive keyboard commands and headless rendering modes.
This repository provides two LiDAR degeneracy datasets (tunnel and fog) in ROS bag format, hosted on Hugging Face. It includes detailed metadata about the sensors (IMU, LiDAR, radar), their extrinsics, topics, and rates. The datasets are intended for research in LiDAR-radar-inertial fusion for challenging environments. The repository contains no code for processing the data, only documentation and links to the raw data.