Open-source alternatives to Lidar

CGAL is a software library that provides a comprehensive collection of computational geometry algorithms and data structures. It is built around a geometry kernel that defines fundamental geometric primitives and operations, enabling the construction of complex geometric objects and the computation of geometric predicates with exact arithmetic for reliable results. The library covers a wide range of geometric computation capabilities, including the construction of convex hulls, triangulations of point sets, and the generation of Voronoi diagrams. It also supports the processing of polygonal m

FAST_LIO is a real-time SLAM system and LiDAR-inertial odometry package designed for simultaneous localization and mapping. It functions as a state estimation engine and 3D mapping tool that fuses LiDAR point clouds with inertial measurement unit data to provide robust robot state estimation. The system utilizes a tightly-coupled sensor fusion approach with an iterative Kalman filter to estimate position and orientation. It distinguishes itself through direct point-to-plane matching, which calculates odometry by matching raw lidar points to the map surface without manual geometric feature ext

The Point Cloud Library is a collection of C++ algorithms designed for filtering, registering, and analyzing large-scale 3D spatial datasets. It provides a framework for 3D point cloud processing, incorporating tools for spatial data filtering and geometric feature estimation. The library includes specialized systems for aligning multiple spatial datasets into a single unified coordinate system and a rendering engine for the visual inspection and analysis of processed point cloud data. It also features tools for calculating spatial descriptors to identify structural patterns and shapes within

Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language descriptions and two-dimensional images. It utilizes diffusion models to synthesize these spatial representations based on text prompts or source images. The project includes specialized tools for refining these outputs, such as a point cloud upsampler to increase the density and resolution of low-resolution models. It also provides a mesh converter that uses distance function regression to transform raw point cloud data into structured 3D meshes. The broader capability surface cove

LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph optimization engine that combines lidar scans and inertial measurement unit data to build 3D point cloud maps and estimate robot trajectories. The system integrates global position factors to align local coordinates with real-world data. It employs loop closure detection to identify previously visited locations, creating constraints in the optimization graph to correct accumulated global drift. The framework covers lidar inertial odometry, point cloud processing, and trajecto

mmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing convolutional neural networks, a toolkit for image and video preprocessing, and a collection of high-performance deep learning vision operators. The project is distinguished by its hardware-accelerated kernels for complex operations such as deformable convolutions and region pooling. It features a configuration-driven framework that allows for the dynamic instantiation of network layers and the registration of custom modules without modifying code. The library covers a broad

Valhalla is an open-source routing engine that calculates optimal paths and travel times using OpenStreetMap data. It is built around a tiled routing graph framework, allowing map data to be organized into small geographic tiles for efficient regional updates and offline routing capability. The project distinguishes itself through a multimodal routing server that combines automobile, pedestrian, bicycle, and public transit modes into single journeys. It includes a GPS trace matching engine to align noisy coordinates to the most probable road network paths and an isochrone and matrix generator

This project is an open-source 3D game engine designed for building high-fidelity games, simulations, and cinematic environments. It functions as a robotics simulation platform with native integration for ROS 2 to model robot controllers and sensors. The engine features a multi-threaded Forward+ physically based renderer that supports hardware-accelerated ray tracing and global illumination. The system is built on a modular extension architecture using Gems to add or replace features without modifying core binaries. It includes a native SDK for AWS cloud integration, enabling IAM authenticati

Construct and use OGC TileMatrixSets (TMS)

A utility to search, download and process Landsat 8 satellite imagery

mundipy is a Python framework for spatial data manipulation

Water Detect Algorithm

Entwine - point cloud organization for massive datasets

Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation

Detect 6-DOF grasp poses in point clouds

Pure Julia reader of lasindex .lax files

Extends LasIO with Laszip integration

Deep Hough Voting for 3D Object Detection in Point Clouds

Processing and gridding spatial data, machine-learning style

OSMnx is a Python library for downloading, modeling, and analyzing street networks and other geospatial features from OpenStreetMap. It enables users to retrieve and work with real-world infrastructure data anywhere in the world, providing tools for network analysis, spatial queries, and visualization. The library offers capabilities for working with urban features such as building footprints, transit stops, and elevation data, along with network statistics like intersection density and circuity. It supports multiple travel modes including driving, walking, and biking, and can calculate short

Using SQLAlchemy with spatial databases

Software for Advanced Spatial Econometrics

GeoPandas is a Python library that extends pandas with native support for geospatial data. It treats geographic geometries—points, lines, and polygons—as a first-class column type within DataFrames, enabling users to store, manipulate, and analyze vector spatial data alongside traditional tabular attributes. The library is built on top of proven geospatial components: it uses Shapely for all geometric operations, Fiona and GDAL for reading and writing standard spatial file formats, PyProj for coordinate reprojection, and an R‑tree spatial index (from Shapely) to accelerate spatial queries. Wh

An Iterative Closest Point (ICP) library for 2D and 3D mapping in Robotics